concerted cultivation in childhood academic …

The Pennsylvania State University

The Graduate School

THE ROLE OF “CONCERTED CULTIVATION” IN CHILDHOOD

ACADEMIC ACHIEVEMENT GROWTH PROCESSES:

CLASS AND RACE DIFFERENCES FROM KINDERGARTEN

THROUGH THIRD GRADE

A Thesis in

Sociology and Demography

by

Jacob E. Cheadle

c© 2005 Jacob E. Cheadle

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

August 2005

The thesis of Jacob E. Cheadle was reviewed and approved∗ by the following:

Paul R. Amato

Head of the Department of Sociology

Professor of Sociology, Demography, and Family Studies

Thesis Advisor, Chair of Committee

George Farkas

Professor of Sociology, Demography, and Education

Nancy S. Landale

Professor of Sociology and Demography

D. Wayne Osgood

Professor of Crime, Law and Justice and Sociology

Suet-ling Pong

Associate Professor of Education, Sociology, and Demography

∗Signatures are on file in the Graduate School.

Abstract

Drawing on longitudinal data from the Early Childhood Longitudinal Study, Kinder-garten Class Cohort of 1998-1999, which follows children from kindergarten entry tothe end of third grade, this study assesses (1) the generalizeability of Lareau’s (2003)notion of ‘concerted cultivation’ and (2) the extent to which this concept mediates raceand social class differences in children’s general knowledge, mathematics, and readingachievement growth. In the first analytic stage, ‘concerted cultivation,’ as identified byLareau (2003) in a recent ethnographic study, is captured as a latent variable using in-dicators of (a) parental participation with school, (b) child participation in organizedleisure activities, (c) and academic resources in the home. Using traditional SEM andIRT modeling techniques, these items are conceptualized as the product of a higher-orderfactor, labeled ‘concerted cultivation,’ which embodies a cultural logic of child-rearing.Not only does the evidence support a generalization of Lareau’s (2003) observations froma small local sample to the U.S. population, the factor structure of concerted cultivationis highly stable over time and the latent construct varies positively with increasing socialclass and advantage on other sociodemographic characteristics, largely in accord withexpectations. The second analytic stage assesses (a) the association between concertedcultivation and children’s academic skills and (b) the role concerted cultivation plays inracial and social class disparities in achievement growth. Findings based on three-levelpiecewise growth models with school-level fixed-effects illustrate that the measure of con-certed cultivation is an important predictor of children’s early academic competencies,particularly at kindergarten entry, while also mediating race and class differences in chil-dren’s growth. Adjusting for concerted cultivation reduces the black-white reading gapto non-significance and mediates the Hispanic-white reading gap by over 40% at kinder-garten entry. Concerted cultivation typically reduces race and social class gaps in thegrowth parameters by 20%-40%, indicating a substantial reduction in coefficient magni-tude, although a significant proportion of race and class differences are left unaccountedfor. After including concerted cultivation along with the full covariate list, black childrencontinually lose ground in mathematics and reading during the school year, while Asianchildren gain significant ground relative to other children over the summertime. Con-sistent with a growing body of research documenting the importance of early childhoodexperiences in shaping later race and social class achievement disparities, this researchfurther points to the need for effective early childhood policy interventions to reducedisparities in children’s academic skill levels at kindergarten entry.

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TABLE OF CONTENTS

List of Figures viii

List of Tables xi

Acknowledgments xiv

1 Motivation & Introduction 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 The Family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3 Test Score Convergence Since 1965 . . . . . . . . . . . . . . . . . . 81.4 Individual-Level Differences in Achievement . . . . . . . . . . . . . 111.5 Social Group Differences: Perspectives . . . . . . . . . . . . . . . . 141.6 The Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171.7 Chapter Previews . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2 Concerted Cultivation 202.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.2 ‘Concerted Cultivation’ . . . . . . . . . . . . . . . . . . . . . . . . . 222.3 Social Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.4 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.4.1 Financial Capital . . . . . . . . . . . . . . . . . . . . . . . . . 272.4.2 Human Capital . . . . . . . . . . . . . . . . . . . . . . . . . . 292.4.3 Cultural Capital . . . . . . . . . . . . . . . . . . . . . . . . . 30

Student Achievement . . . . . . . . . . . . . . . . . . . . . . 352.4.4 Social Capital . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.5 The School & Classroom . . . . . . . . . . . . . . . . . . . . . . . . 422.6 The Summertime . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442.7 Summary & Research Questions . . . . . . . . . . . . . . . . . . . . 46

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3 Data & Methods 493.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.1.1 The Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503.1.2 Dependent Variables . . . . . . . . . . . . . . . . . . . . . . . 51

General Knowledge Achievement . . . . . . . . . . . . . . . 53Mathematics Achievement . . . . . . . . . . . . . . . . . . . 54Reading Achievement . . . . . . . . . . . . . . . . . . . . . . 56

3.1.3 The Sample & Control Variables . . . . . . . . . . . . . . . . 57Race & Social Class . . . . . . . . . . . . . . . . . . . . . . 57Child Variables . . . . . . . . . . . . . . . . . . . . . . . . . 59Family Variables . . . . . . . . . . . . . . . . . . . . . . . . 60Additional Covariates . . . . . . . . . . . . . . . . . . . . . . 61

3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623.2.1 Concerted Cultivation . . . . . . . . . . . . . . . . . . . . . . 623.2.2 Academic Achievement . . . . . . . . . . . . . . . . . . . . . 64

4 Concerted Cultivation 684.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684.2 Confirmatory Factor Analysis . . . . . . . . . . . . . . . . . . . . . 69

4.2.1 The Covariate List . . . . . . . . . . . . . . . . . . . . . . . . 694.2.2 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714.2.3 1 Wave CFA . . . . . . . . . . . . . . . . . . . . . . . . . . . 734.2.4 3 Wave CFA . . . . . . . . . . . . . . . . . . . . . . . . . . . 754.2.5 Summary & Notes . . . . . . . . . . . . . . . . . . . . . . . . 79

4.3 Predicting Concerted Cultivation . . . . . . . . . . . . . . . . . . . 804.4 Concerted Cultivation: The Distribution . . . . . . . . . . . . . . . 814.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

5 General Knowledge Achievement 905.1 General Knowledge Achievement Growth . . . . . . . . . . . . . . . 905.2 General Knowledge Growth . . . . . . . . . . . . . . . . . . . . . . 91

5.2.1 Non-Centered Growth Models . . . . . . . . . . . . . . . . . . 93Initial Status . . . . . . . . . . . . . . . . . . . . . . . . . . 94Kindergarten Slope . . . . . . . . . . . . . . . . . . . . . . . 97Summer Slope . . . . . . . . . . . . . . . . . . . . . . . . . . 971st Grade Slope . . . . . . . . . . . . . . . . . . . . . . . . . 98Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

5.2.2 Group-Mean Centered Models . . . . . . . . . . . . . . . . . 99Initial Status . . . . . . . . . . . . . . . . . . . . . . . . . . 103The Slope Parameters . . . . . . . . . . . . . . . . . . . . . 103

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5.2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1065.3 Interactions with Concerted Cultivation . . . . . . . . . . . . . . . . 110

5.3.1 Race x Concerted Cultivation Interactions . . . . . . . . . . . 1105.3.2 SES x Concerted Cultivation Interactions . . . . . . . . . . . 1135.3.3 SES x Race x Concerted Cultivation . . . . . . . . . . . . . . 117

5.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

6 Math Achievement 1226.1 Math Achievement Growth . . . . . . . . . . . . . . . . . . . . . . . 123

6.1.1 Non-Centered Growth Models . . . . . . . . . . . . . . . . . . 125Initial Status . . . . . . . . . . . . . . . . . . . . . . . . . . 126The Kindergarten Slope . . . . . . . . . . . . . . . . . . . . 129The Summer Slope . . . . . . . . . . . . . . . . . . . . . . . 130The 1st Grade Slope . . . . . . . . . . . . . . . . . . . . . . 130The 2nd − 3rd Grade Slope . . . . . . . . . . . . . . . . . . . 131Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

6.1.2 Group-Mean Centered Models . . . . . . . . . . . . . . . . . 139Initial Status . . . . . . . . . . . . . . . . . . . . . . . . . . 139The Slope Parameters . . . . . . . . . . . . . . . . . . . . . 147Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

6.2 Interactions with Concerted Cultivation . . . . . . . . . . . . . . . . 1496.2.1 Race x Concerted Cultivation Interactions . . . . . . . . . . . 1496.2.2 SES x Concerted Cultivation Interactions . . . . . . . . . . . 1526.2.3 SES x Race x Concerted Cultivation . . . . . . . . . . . . . . 155

6.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

7 Reading Achievement 1597.1 Reading Achievement Growth . . . . . . . . . . . . . . . . . . . . . 160

7.1.1 Non-Centered Growth Models . . . . . . . . . . . . . . . . . . 163Initial Status . . . . . . . . . . . . . . . . . . . . . . . . . . 163The Kindergarten Slope . . . . . . . . . . . . . . . . . . . . 166The Summer Slope . . . . . . . . . . . . . . . . . . . . . . . 167The 1st Grade Slope . . . . . . . . . . . . . . . . . . . . . . 167The 2nd − 3rd Grade Slope . . . . . . . . . . . . . . . . . . . 168Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

7.1.2 Group-Mean Centered Models . . . . . . . . . . . . . . . . . 175Initial Status . . . . . . . . . . . . . . . . . . . . . . . . . . 176The Slope Parameters . . . . . . . . . . . . . . . . . . . . . 178Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

7.2 Interactions with Concerted Cultivation . . . . . . . . . . . . . . . . 186

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7.2.1 Race x Concerted Cultivation Interactions . . . . . . . . . . . 1867.2.2 SES x Concerted Cultivation Interactions . . . . . . . . . . . 1897.2.3 SES x Race x Concerted Cultivation . . . . . . . . . . . . . . 192

7.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

8 Discussion & Conclusion 1978.1 Re-Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1978.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

8.2.1 Concerted Cultivation & Cultural Capital . . . . . . . . . . . 1998.2.2 Operationalizing ‘Concerted Cultivation’ . . . . . . . . . . . . 2028.2.3 Academic Achievement . . . . . . . . . . . . . . . . . . . . . 208

8.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213

A Appendix 226A.1 Supplementary General Knowledge Tables . . . . . . . . . . . . . . 227A.2 Supplementary Math Achievement Tables . . . . . . . . . . . . . . 234A.3 Supplementary Reading Achievement Tables . . . . . . . . . . . . . 242

Bibliography 250

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LIST OF FIGURES

1.1 How Unequal Schools Can Serve as Equalizers (Downey et al. 2004:614) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2 NAEP Math Achievement Scores for 9 Year-Olds by Race and FreeLunch Eligibility (as a Social Class Indicator) for Selected Years . . 10

1.3 NAEP Reading Achievement Scores for 9 Year-Olds by Race andFree Lunch Eligibility (as a Social Class Indicator) for Selected Years 11

3.1 Graphical Depiction of Children’s Academic Growth Model . . . . 66

4.1 Graphical Depiction of the Concerted Cultivation CFA . . . . . . . 724.2 Item Characteristic Curves for Child Activities and Parent Partici-

pation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764.3 Between-Wave Correlations for ‘Concerted Cultivation.’ . . . . . . . 774.4 Concerted Cultivation by Race . . . . . . . . . . . . . . . . . . . . . 854.5 Concerted Cultivation by Social Class . . . . . . . . . . . . . . . . . 864.6 Concerted Cultivation by Social Class & Race . . . . . . . . . . . . 87

5.1 Graphical Depictions of Race Differences in Children’s General Knowl-edge Growth from Kindergarten Entry Through 1st Grade from Ta-ble 5.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

5.2 Graphical Depictions of Social Class Differences in Children’s Gen-eral Knowledge Growth from Kindergarten Entry Through 1st Gradefrom Table 5.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

5.3 Graphical Depictions of Differences in Children’s General Knowl-edge Growth from Kindergarten Entry Through 1st Grade by Con-certed Cultivation from Table 5.2 . . . . . . . . . . . . . . . . . . . 102

5.4 Graphical Depictions of Group-Centered Race Differences in Chil-dren’s General Knowledge Growth from Kindergarten Entry Through1st Grade from Table 5.3 . . . . . . . . . . . . . . . . . . . . . . . . 107

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5.5 Graphical Depictions of Group-Centered Social Class Differencesin Children’s General Knowledge Growth from Kindergarten EntryThrough 1st Grade from Table 5.3 . . . . . . . . . . . . . . . . . . . 108

5.6 Graphical Depictions of Group-Centered Differences in Children’sGeneral Knowledge Growth from Kindergarten Entry Through 1st

Grade by Concerted Cultivation from Table 5.3 . . . . . . . . . . . 109

6.1 Graphical Depictions of Children’s Math Growth from KindergartenEntry Through 3rd Grade by Race from Table 6.2 . . . . . . . . . . 133

6.2 Graphical Depictions of Race Differences from Whites’ in Children’sMath Growth from Kindergarten Entry Through 3rd Grade fromTable 6.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

6.3 Graphical Depictions of Children’s Math Growth from KindergartenEntry Through 3rd Grade by Social Class from Table 6.2 . . . . . . 135

6.4 Graphical Depictions of Social Class Differences in Children’s MathGrowth from Kindergarten Entry Through 3rd Grade from Table 6.2 136

6.5 Graphical Depictions of Children’s Math Growth from KindergartenEntry Through 3rd Grade by Concerted Cultivation from Table 6.2 137

6.6 Graphical Depictions of Differences in Children’s Math Growth fromKindergarten Entry Through 3rd Grade by Concerted Cultivationfrom Table 6.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

6.7 Graphical Depictions of Children’s Math Growth from KindergartenEntry Through 3rd Grade by Race from Table 6.2, Group-MeanCentered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

6.8 Graphical Depictions of Race Differences from Whites’ in Children’sMath Growth from Kindergarten Entry Through 3rd Grade fromTable 6.3, Group-Mean Centered . . . . . . . . . . . . . . . . . . . 143

6.9 Graphical Depictions of Children’s Math Growth from KindergartenEntry Through 3rd Grade by Social Class from Table 6.3, Group-Mean Centered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

6.10 Graphical Depictions of Social Class Differences in Children’s MathGrowth from Kindergarten Entry Through 3rd Grade from Table6.3, Group-Mean Centered . . . . . . . . . . . . . . . . . . . . . . . 145

6.11 Graphical Depictions of Children’s Math Growth from KindergartenEntry Through 3rd Grade by Concerted Cultivation from Table 6.3,Group-Mean Centered . . . . . . . . . . . . . . . . . . . . . . . . . 146

6.12 Graphical Depictions of Differences in Children’s Math Growth fromKindergarten Entry Through 3rd Grade by Concerted Cultivationfrom Table 6.3, Group-Mean Centered . . . . . . . . . . . . . . . . 147

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7.3 Graphical Depictions of Children’s Math Growth from KindergartenEntry Through 3rd Grade by Social Class from Table 7.2 . . . . . . 172






7.9 Graphical Depictions Children’s Math Growth from KindergartenEntry Through 3rd Grade by Social Class from Table 7.3 . . . . . . 182




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LIST OF TABLES

2.1 Typology of Differences in Child Rearing . . . . . . . . . . . . . . . 25

3.1 ECLS-K Weighted Sample Means, Standard Deviations, and Proportions for theTotal Sample, by Race and Social Class, for the Control Variables at Kinder-garten Entry (Approximate N = 14, 152) . . . . . . . . . . . . . . . . . . 58

4.1 Measures Used to Specify the Concerted Cultivation CFA . . . . . . . . . . 704.2 Results for the Kindergarten Concerted Cultivation CFA. The ‘Discrimination’

Parameters are Analogous to Factor Loadings Standardized with Reference tothe Latent Variable, and the ‘Difficulty’ Parameters are Intercepts . . . . . . 74

4.3 Concerted Cultivation CFA at Kindergarten, First Grade, and Third Grade.The Item Discrimination Parameters are Standardized with Respect to the La-tent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

4.4 SEM Partially-Standardized Regression Coefficients for Models Predicting Con-certed Cultivation (N = 14, 152) . . . . . . . . . . . . . . . . . . . . . . 82

4.5 Concerted Cultivation Distributional Characteristics by Race andSocial Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

5.1 Null Growth Models for General Knowledge Achievement Scores(IRT) from Kindergarten Entry Until Spring of Third Grade . . . . 92

5.2 Growth Models for General Knowledge Achievement Scores (IRT) from Kinder-garten Entry Until Spring of Third Grade by Race, Social Class, ConcertedCultivation, and Selected Covariates (ECLS-K) . . . . . . . . . . . . . . . 95

5.3 Group-Mean Centered Growth Models for General Knowledge AchievementScores (IRT) from Kindergarten Entry Until Spring of Third Grade by Race,Social Class, Concerted Cultivation, and Selected Covariates (ECLS-K) . . . . 104

5.4 Race Interactions with Concerted Cultivation Growth Models for General Knowl-edge Achievement Scores (IRT) from Kindergarten Entry Until the Spring ofThird Grade (ECLS-K) . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

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5.5 Social Class Interactions with Concerted Cultivation Growth Models for GeneralKnowledge Achievement Scores (IRT) from Kindergarten Entry Until the Springof Third Grade (ECLS-K) . . . . . . . . . . . . . . . . . . . . . . . . . 115

5.6 Race & Class Interactions with Concerted Cultivation Growth Models for Gen-eral Knowledge Achievement Scores (IRT) from Kindergarten Entry Until theSpring of First Grade (ECLS-K) . . . . . . . . . . . . . . . . . . . . . . 118

6.1 Null Growth Models for Math Achievement Scores (IRT) from Kinder-garten Entry Until Spring of Third Grade . . . . . . . . . . . . . . 124

6.2 Growth Models for Math Achievement Scores (IRT) from Kindergarten EntryUntil Spring of Third Grade by Race, Social Class, Concerted Cultivation, andSelected Covariates (ECLS-K) . . . . . . . . . . . . . . . . . . . . . . . 127

6.3 Group-Mean Centered Growth Models for Math Achievement Scores (IRT) fromKindergarten Entry Until Spring of Third Grade by Race, Social Class, ‘Con-certed Cultivation,’ and Selected Covariates (ECLS-K) . . . . . . . . . . . . 140

6.4 Race Interactions with Concerted Cultivation Growth Models for Math Achieve-ment Scores (IRT) from Kindergarten Entry Until the Spring of Third Grade(ECLS-K) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

6.5 Social Class Interactions with Concerted Cultivation Growth Models for MathAchievement Scores (IRT) from Kindergarten Entry Until the Spring of ThirdGrade (ECLS-K) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

6.6 Race & Class Interactions with Concerted Cultivation Growth Models for Math-ematics Achievement Scores (IRT) from Kindergarten Entry Until the Springof Third Grade (ECLS-K) . . . . . . . . . . . . . . . . . . . . . . . . . 156

7.1 Null Growth Models for Reading Achievement Scores (IRT) fromKindergarten Entry Until Spring of Third Grade . . . . . . . . . . . 161

7.2 Growth Models for Reading Achievement Scores (IRT) from Kindergarten EntryUntil Spring of Third Grade by Race, Social Class, Concerted Cultivation, andSelected Covariates (ECLS-K) . . . . . . . . . . . . . . . . . . . . . . . 164

7.3 Group-Mean Centered Growth Models for Reading Achievement Scores (IRT)from Kindergarten Entry Until Spring of Third Grade by Race, Social Class,Concerted Cultivation, and Selected Covariates (ECLS-K) . . . . . . . . . . 177

7.4 Race Interactions with Concerted Cultivation Growth Models for Reading Achieve-ment Scores (IRT) from Kindergarten Entry Until the Spring of Third Grade(ECLS-K) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

7.5 Social Class Interactions with Concerted Cultivation Growth Models for Read-ing Achievement Scores (IRT) from Kindergarten Entry Until the Spring ofThird Grade (ECLS-K) . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

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7.6 Race & Class Interactions with Concerted Cultivation Growth Models for Read-ing Achievement Scores (IRT) from Kindergarten Entry Until the Spring ofThird Grade (ECLS-K) . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

A.1 Proportion Reduction in Coefficient Magnitude Between Models forGeneral Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . 227

A.2 Partially-Standardized with Respect to the Outcome Regression Co-efficients for Table 5.2, General Knowledge . . . . . . . . . . . . . . 228

A.3 Partially-Standardized with Respect to the Outcome Regression Co-efficients for Table 5.3, General Knowledge, Group-Mean Centered . 229

A.4 Supplementary Models for General Knowledge Achievement withthe Full Covariate List . . . . . . . . . . . . . . . . . . . . . . . . . 230

A.5 Proportion Reduction in Coefficient Magnitude Between Models forMath Achievement . . . . . . . . . . . . . . . . . . . . . . . . . . . 234

A.6 Partially-Standardized with Respect to the Outcome Regression Co-efficients for Table 6.2, Mathematics Achievement . . . . . . . . . . 235

A.7 Partially-Standardized with Respect to the Outcome RegressionCoefficients for Table 6.3, Mathematics Achievement, Group-MeanCentered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236

A.8 Supplementary Models for Mathematics Achievement with the FullCovariate List . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237

A.9 Proportion Reduction in Coefficient Magnitude Between Models forReading Achievement . . . . . . . . . . . . . . . . . . . . . . . . . . 242

A.10 Partially-Standardized with Respect to the Outcome Regression Co-efficients for Table 7.2, Reading Achievement . . . . . . . . . . . . . 243

A.11 Partially-Standardized with Respect to the Outcome Regression Co-efficients for Table 7.3, Reading Achievement, Group-Mean Centered 244

A.12 Supplementary Models for Reading Achievement with the Full Co-variate List . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245

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Acknowledgments

The dissertation is a large project, unfolding not only in front of the computer orin conversation with engaging readings, but also in offices and at bars, with familyand friends and mentors. It is a topic for discussion or monologues that sometimesputs the other parties to sleep, and at other times simply bores them to politelyshed inner-tears. For all the prosaic conversation those writing dissertations imposeon other people, those other people probably never realize how lucky they are tohave the conversation and not word after word after word of text that evolve fromthe process, each little paragraph mounting the committee member’s backs withall the panache of little monkeys. Nobody gets it worse than the Advisor. Onemight be tempted to say, “That’s their job.” But the truth is, not all advisors arecreated equal, and I’ve been lucky in this regard.

I would like to thank my Advisor, Paul Amato, who, despite becoming Depart-ment Chair this last year, found time for weekly meetings and usually managedto get my chapter drafts to me on time. Not only was Paul a constant source offeedback, his management style was extremely patient, despite worries he no doubtnurtured that I would not finish. At some point one must quit dreaming up newmodels and reorganizing the substantive focus of the dissertation, and I know hequietly worried that I would never transition from that phase to the “I just wantthe thing done” stage. Not all advisors give their students so much freedom, andalthough I took a little longer to complete the dissertation than some, I’m thankfulfor having had the time and support to wander through the statistical issues whichwere apparent early on, and to which I was only able to incorporate partial solu-tions in this work. Even when the methods component over-awed the substantivefocus of the dissertation, Paul was supportive, although he often looked at me likeI was insane when I wandered in to his office babbling about a 24hr model runin WinBUGS. While powerful computers become increasingly ubiquitous, good dis-sertation chairs remain a precious commodity; they are the artists of professional

xiv

development.Paul, of course, was not alone in nurturing the interests that gave rise to this

dissertation. Without relying on any implicit ranking, I note two more faculty inalphabetical order, George Farkas and Sean Reardon. It was George’s stimulatinglectures that first interested me in student achievement, it was his idea that I usethe ECLS-K, it was George who gave me the last round of feedback on the finaldrafts of my dissertation, and it was at his suggestion that I met Sean. Aftermeeting Sean, I joined him on a project with Claudia Galindo that presaged myown work. Just as George’s conceptual discussions nurtured my growing interestsin childhood academic achievement, Sean’s approach to modeling the phenomena,as well as his remarkable ability to consider hypothesis testing, had a profoundinfluence on my methodological development. I suppose that Paul, George, andSean are the Academic Trinity to which this dissertation owes its narrow life, andto which much of my own development is indebted.

Other committee member played roles at various stages in the progress of mydissertation. Things get a little tangled at this point because Sean left PSU forStanford during the final year of work, necessitating some changes. The originalcommittee consisted of Paul Amato, George Farkas, Sean Reardon, Nancy Landale,and Bob Schoen. My thanks to both Nancy and Bob who were present for theinitial draft of my proposal. Before Sean left, he was both an outside committeemember and my “methodologist,” so Wayne Osgood and Suet-ling Pong joinedand finished the project after Sean left. My thanks to both Wayne and Suet-lingas well, who came on board late in the project.

Two more Penn State faculty played notable roles, although they were notexplicitly involved in the project. At a critical juncture, during which I presenteda job-talk based upon an earlier formation of the dissertation, Mark Hayward(along with George and Bob), offered some very helpful advice on the conceptualframing of my project. It was after this job talk that I picked up Lareau, seekinga richer theoretical framework around which to orient my work. To Mark, “Thankyou for coming to that talk and giving me some very serious advice on ‘windowdressing.’” Francis Dodoo, who I TA’d for in the Spring, was also present at thejob talk. My sincere thanks to Francis, however, stem from the freedom he allowedme while I worked with him.

Faculty may have the most conspicuous impacts on a dissertation, but they arenot the only influences, nor are they necessarily the most important. Through thisprocess, I’ve been lucky to have an understanding and supportive partner, BridgetGoosby, one who has already survived this performance herself. She knows I’mthankful, but now she can read it and know that at some point maybe as manyas six or seven people will have read it too. Two close friends here at Penn Statewere also important; friends who don’t fall asleep when you get too academic are

xv

as rare and precious as good advisors, and I extend my many thanks to (again,alphabetical) Paul-Philippe Pare and Mike Stout for their camaraderie and many(and ongoing) discussions.

Some few thousand miles back West live my family and the friends of adoles-cence and young adulthood. My parents, Richard and Suzanne Cheadle, and mysister, Lisa, have never hesitated to share their pride in me with me, nor have theyhesitated ever to support or encourage me. Thank you. The same goes through formy surviving grandmother, Elsie Olson, as well as the bulk of my extended-family.They’ve been asking for years when I’ll be finished; now, I guess. The same goes formy many friends back home, Eric McDaniel, Jim and Nicole Morrell, Eric Olson,Dan Schwert, Donney and Becky Legge, and Phil Lyne, to choose some names,perhaps too selectively. Whenever I visit back home, I also visit the faculty whofirst put forth the conceptual fireworks that attracted me to sociology. Thank youfor your many conversations Ed and Karen Stephan, and your early mentoringLucky Tedrow and Jay Teachman.

In addition to the excellent support offered by the Penn State sociology de-partment and the Population Research Institute, I was also fortunate to receiveexternal funding that helped to support this research. First and foremost: Thisresearch was supported by a grant from the American Educational Research Associa-tion which receives funds for its “AERA Grants Program” from the National ScienceFoundation and the National Center for Education Statistics of the Institute of Ed-ucation Sciences (U.S. Department of Education) under NSF Grant #REC-9980573.Opinions reflect those of the author and do not necessarily reflect those of the grantingagencies. Secondly, the RGSO here at PSU purchased Mplus and some key booksthat allowed me to complete the analytic portion of the dissertation.

To all, thank you.

xvi

CHAPTER

ONE

Motivation & Introduction

1.1 Introduction

With passage of the Elementary and Secondary Education Act Reauthorization of

2001, commonly known as “No Child Left Behind (NCLB),” questions surrounding

social group1 disparities in children’s academic achievement have received new

impetus. The “high stakes” paradigm driving the development of NCLB has moved

the testing of academic competencies into the public spotlight in an unprecedented

fashion. By stipulating financial consequences for low-performing schools which

are unable to incrementally improve student performance over time, NCLB has

given the U.S. education system a new sense of urgency in its orientation towards

academic benchmarking via standardized testing. Given the potential financial

deficits for schools which are unable to meet state defined goals, there is a real fear

that the structural consequences of NCLB will ultimately prove to be yet another

detriment in the lives of children who are already significantly disadvantaged.

NCLB is a fundamentally optimistic piece of legislation, positing that academic

achievement gaps for different social groups can be minimized through a focus on

district-wide and school-level accountability. This raises an important question:

Do schools have the ability to erase race and social class achievement gaps? The

short answer to this question is simple: To the extent that schools are responsible

1I will often use “social group” when discussing broad social categorizations or when referring,in particular, to multiple social group categorizations such as ‘race’ and ‘social class.’

2

for the gaps, they should be able to eliminate them. As a piece of legislation, NCLB

does not explicitly posit an explanatory developmental model, yet there are strong

assumptions about the role environments play in academic achievement processes.

NCLB, by design, suggests that school and classroom characteristics which are

amenable to change are at fault for minority and low social class children’s gaps

in math, reading, and science.2

What is the role the formal education system plays in social group achievement

gaps? Schools, it would seem, must play an important role if they are to ameliorate

social group disparities. What if, in fact, family advantage is more strongly related

to academic achievement gaps, however? This is not a new question, and no

doubt policy makers and scholars alike were startled at the findings of the famous

Coleman Report (Coleman et al. 1966; see also Coleman 1972) which found, in

fact, that family background was the single most important predictor of student

achievement, followed by the characteristics of the student body. The evidence

demonstrating large skill differences between children when they are very young is

strong (Farkas and Beron 2004; see also Lee and Burkam 2003 for a discussion of

skill differences at kindergarten entry), and the family implication is well supported

in the developmental literature (e.g. Phillips, Brooks-Gunn, Duncan, Klebanov,

and Crane 1998a; Phillips, Crouse, and Ralph 1998b). With the attention paid to

schools in achievement processes increasing, researchers and policy makers would

be remiss to disregard other important sources of variation in children’s lives,

particularly during the often neglected early school years (Alexander and Entwistle

1989: 1).

More recent research suggests, in the words of Downey, von Hippel, and Broh

(2004), that schools are the great equalizer,3 ameliorating out-of-school disadvan-

tage. This claim is supported by evidence indicating that children uniformly grow

faster during the school year than over the summer time, variability in growth rates

are smaller during the school year than during the summer, and in many cases so-

2The School Readiness Act of 2003, which states that Head Start should be designed toprovide education-oriented training to prepare children for school, points out that the Bushadministration is not unaware of the importance of differences in levels of school readiness acrosssocial groups. See http://www.acf.hhs.gov/programs/hsb/pdf/hs reauthorization.pdf.

3This is a allusion to Bowles and Gintis’s (1976) seminal work, Schooling in Capitalist America:Educational Reform and Contradictions of Economic Life.

3

cial group gaps also grow at smaller rates.4 In fact, research suggests that about

35% of the variance in children’s test scores at kindergarten entry resides between

schools, while about 15% of the variance in children’s growth rates is between

schools (Reardon 2003). This partitioning of the variance components suggests

that although children in different schools have disparate skills at kindergarten

entry and grow at different rates during school, there is more variability in growth

rates between children within schools. However, nearly 70% of the between-school

variance at kindergarten entry and about 15% and 26% of the between-school vari-

ance in kindergarten and first grade learning rates, respectively, is due primarily to

differences in the racial and social class composition between schools (Downey et al.

2004).5 Thus, much of the variability in between-school growth differences is due

to compositional differences, factors whose distributions are broadly structural and

largely outside the control of school policy. In addition, the school-compositional

disparities are themselves the aggregation of between-child social group disparities,

which indicates that much of the between-school differences in children’s growth

is due to extra-school factors such as the family.6

The studies cited above (e.g. Reardon 2003; Downey et al. 2004) show that

schools greatly increase children’s learning rates, but also demonstrate smaller vari-

ability in children’s learning growth across schools in relation to non-school factors.

As depicted in figure 1.1, which is taken from Downey and colleagues (2004: 614),

because there is less variability in school than non-school environments, disadvan-

taged children may actually benefit more from school than advantaged children. It

is important to note that the arrows in the figure represent growth rates relative

to out-of-school rates for each group, and not that disadvantaged children grow

faster when in school. When considered in this light, it is difficult to imagine how

schools are supposed to appreciably eliminate race and social class gaps when they

do not appear to be the primary source of social group difference. Without an even

4Black-white differences in growth are consistently larger during school, however.5Downey et al. (2004) do not compute the between-school R2 values in their paper. However,

since they do not group-mean center the between-student variables in their three-level model,their coefficient estimates are in fact correlated with their between-school random effects (seeRaudenbush and Bryk 2002). Decreases in the between-school variance components in this caseindicate the proportion of variance which would be explained if the group-means were includedin the between-school model.

6These between-school compositional differences could also proxy for other school character-istics.

4

Figure 1.1. How Unequal Schools Can Serve as Equalizers (Downey et al. 2004: 614)

Non-School Environment School Environment

High quality homes

and neighborhoods

Extremely poor homes

and neighborhoods

Excellent Schools

Poor Schools

Note: Because non-school environments vary more than school environments, a child from a disadvantagednon-school environment can attend a disadvantaged school and yet still enjoy a greater school benefit than achild from an advantaged non-school environment who attends an advantaged school.

larger, more comprehensive policy overhaul than NCLB, it is also unlikely that the

quality of schools advantaged and disadvantaged children attend will be equalized.

One certainly has a difficult time imagining a disadvantaged school populated by

children from advantaged families. The implication is that, at minimum, after

equalizing school environments, disadvantaged children would need to outperform

advantaged children who attend schools of the same quality, which is an unlikely

proposition considering children’s early school disparities.

Given the relatively modest proportion of the variance in children’s learning

rates that is due to schools,7 and the fact that a significant proportion of this

variance is in fact due to school characteristics outside of the school’s control, the

pertinent question is: What non-school factors are most importantly implicated in

children’s social group differences in academic competencies? In a recent ethno-

graphic study, Lareau (2003) reported pronounced social class differences in the

7To note that the between-school variance components are smaller than the between-childcomponents should not be taken to imply that schools are not important. They are. Schoolshave very large impacts on children’s learning, as one would expect, but the impacts are notas varied as one might expect either, even after considering the disparities that exist betweenschools (e.g. Kozol 1991).

5

way parents organize their own lives around their children’s, and in the way that

parents seek to configure their children’s lives, largely by structuring time outside

of school and through language use. According to Lareau (2002, 2003), parents

of different social classes seek to cultivate their children with strikingly dissimilar

strategies, with corresponding consequences for the skills and abilities children de-

velop. Lareau’s (2003) typology identified two categorically defined approaches to

child rearing, concerted cultivation and the accomplishment of natural growth.8

Because children begin school with acute skill dissimilarities, disadvantaged

children would need to learn at faster rates than their advantaged counterparts

when school is in session. This seems unlikely, and without great attention to

early childhood learning processes, NCLB will be a failure in reducing social group

disparities.9 The implication is clear: Educational policy needs to develop a viable

out-of-school, early childhood component which reduces children’s early disparities.

Understanding the family pathways through which initial skill differentials develop

and propagate over time is crucial in this regard (see Farkas and Beron 2003).

In this dissertation, I use a new nationally representative data set, the Early

Childhood Longitudinal Study, Kindergarten Class Cohort of 1998 (ECLS-K), to

assess the implications of a parenting strategy, which Lareau terms ‘concerted cul-

tivation,’ for children’s general knowledge, mathematics, and reading skill growth

from kindergarten entry through the spring of third grade. The research is orga-

nized around four fundamental questions: (1) Is it possible to operationalize ‘con-

certed cultivation,’ and is concerted cultivation identifiable in the population? (2)

How does the distribution of concerted cultivation vary across population groups

and subgroups? (3) What role does concerted cultivation play in race and so-

cial class differences in children’s general knowledge, mathematics, and reading

achievement at kindergarten entry? (4) What role does concerted cultivation play

in the temporal pattern of children’s growth in general knowledge, mathematics,

and reading skills once school begins?

8These concepts are defined in Chapter 2.9It must be noted, however, that NCLB could still be a success in one sense if it results in

system-wide increased performance.

6

1.2 The Family

Research suggests that early skill differentials among children accumulate over the

life-course (Farkas and Beron 2004) with broad implications for educational (Hart

and Risley 1995; Ensmigner and Slusarcik 1992; Alexander, Entwisle, and Horsey

1997; Teachman 1997; Teachman 1996; Jimerson, Egeland, Sroufe, and Carlson

2000), labor market (Murnane, Willett, Duhaldeborde, and Tyler 2001; Kerck-

hoff, Raudenbush, and Glennie 2001; Raudenbush and Kasim 1998) and general

life outcomes, implicating academic competencies in intergenerational poverty and

disadvantage transmission (Farkas 2003). Given the broad reach of educational

inequalities over the life course and across generations, it is not surprising that the

education system functions under constant scrutiny.

However, to the extent that family environments differ across social groups

and produce disparate achievement trajectories, early childhood experiences are

incriminated broadly over the life course, and consequently, across generations

too. Schools are not the sole arbiter of children’s academic competencies. The

reading and mathematics skills developed first within the family, then over the early

educational years, are cumulative, so children who do not acquire basic reading

and mathematics skills at the onset risk falling ever farther behind (Entwisle and

Alexander 1989, 1993). To the extent that poor and minority families are unable to

encourage the development of a strong cognitive foundation, do their children enter

and pass through the schooling system with important achievement disadvantages

that are the consequences of preschool experiences? Upon school entry, does the

role of the family diminish and fade to insignificance over time?

In Unequal Childhoods: Class, Race, and Family Life, Lareau (2003) reports

differing parental perceptions by social class on the roles they as parents are ex-

pected to play in facilitating their children’s cognitive and noncognitive skills. This

detailed ethnographic research on twelve families demonstrates how the cultural

resources of parents can have powerful impacts on children’s life chances, drawing

into relief the diverse pathways through which the social stratification of family

life and childrearing coalesce to produce persistent inequalities in educational ex-

periences across diverse social groups. Higher class parents engage in what Lareau

termed ‘concerted cultivation’ in deliberate attempts to foster their children’s cog-

7

nitive and social skills, whereas less advantaged parents engaged in a collection

of practices she termed ‘the accomplishment of natural growth.’ Both cultural

repertoires represent parenting strategies and embody notions of cultural capital

as reflected in parents’ skills, habits, and styles (see Swidler 1986; Farkas 1990;

1996). Within an intergenerational framework, it is these parenting behaviors

through which parents transmit their human and social capital to their children,

perpetuating advantage and disadvantage across generations.

Parents vary importantly in the cognitive assets and the quantity of financial

and human capital they are able to employ for the benefit of their children’s devel-

opment. Do differences in the environments that parents are able to create for their

children in terms of the nurturative content, substance of their interactions, and

the materials they are able to make available create and perpetuate social group

differences in academic achievement? Parental styles of interaction, approaches

to learning, behavioral patterns, and habits of daily routines vary considerably.

Although social group differences in cognitive development are well documented

and implicated in intra- and intergenerational outcomes (Farkas 2003), it is less

certain how early differences in cognitive achievement propagate over time.

The research presented in later chapters is concerned with relating concerted

cultivation to child cognitive outcomes. One general way of asking the motivating

question is, “What role does the parenting strategy Lareau (2003) terms ‘concerted

cultivation’ play in fostering racial and socioeconomic disparities in childhood aca-

demic achievement?” Another way to frame the inquiry is, “How does concerted

cultivation influence children’s learning trajectories, and what role do these prac-

tices play in creating or perpetuation racial and socioeconomic outcome dispari-

ties?” Although educational researchers, sociologists, and developmental and fam-

ily psychologists have dedicated considerable time and effort to related inquiries,

this work is, to my knowledge, the first attempt to test Lareau’s (2003) model

using nationally representative data, although Lareau’s model is in many respects

consistent with approaches drawing upon the HOME (Bradley 1985) inventories in

the NLSY data (e.g. Farkas and Beron 2004; Guo and Harris 2000; Brooks-Gunn,

Klebanov, and Duncan 1996; Smith, Brooks-Gunn, and Klebanov 1997; Phillips,

Crouse, Ralph 1998a).

8

1.3 Test Score Convergence Since 1965

Cognitive achievement and IQ measures have shown persistent racial and socioe-

conomic differences over time. Although the original tests were biased by both

race and social class content, recent tests have significantly reduced this liability

(see Jencks 1998 for a discussion of racial bias in testing). While researchers’ test-

ing apparatus has improved, however, many of the social factors contributing to

racial and socioeconomic differences in cognitive achievement remain unmitigated.

Asian students generally score the highest on achievement tests (Sun 1998; Sue

and Okazaki 1990), for which they have been popularly labeled the “model minor-

ity,” followed by whites, and a variable ordering of black and Hispanic students.

Although Asian and Hispanic groups have long histories in the United States, as

aggregates they have only recently begun to be adequately represented in survey

data. Limitations remain, however. With the possible exception of Mexican Amer-

icans, the option of disaggregating Asian and Hispanic into their heterogeneous

constituents is generally not viable due to sample limitations. For these reasons,

longitudinal studies of racial disparities in test scores have focused primarily on

black and white students.

Using every major national survey of high school students since 1965, Hedges

and Nowell (1998), in the Black-White Test Score Gap, demonstrate that racial

differences in test scores have decreased over time (see also Kao and Thompson

2003; Alexander 1997; Cook and Evans 2000; Hauser 1995). However, black stu-

dents are still underrepresented in the upper tails of the achievement distribution,

a finding which holds across all types of tests (academic and vocational), and does

not seem to be changing. Grissmer, Flanagan, and Williamson (1998), in the same

volume, focus on why the black-white gap narrowed using the National Assessment

of Educational Progress (NAEP). According to the NAEP data, the gap decreased

more for adolescents (13 and 17 year-olds) than children (9 year-olds) throughout

the 1970’s and 1980’s. However, in the first six years of the 1990’s the gap between

white and black adolescents increased, although scores remained higher than they

were in the 1970’s.

Black children gained more than adolescents over the 1970’s but less during

the 1980’s. Except for a small increase in black children’s math scores relative

9

to whites between 1988 and 1996, black cohorts have registered small gains or

declines. However, Grissmer and his colleagues (1998) report that those gains

made by children appear to be sustained, and may even have increased over time.

The test score variance in mathematics fell for both black and white students be-

cause lower-scoring students gained more than their higher-scoring peers (Grissmer

et al. 1998). Among 9 and 13 year-olds, however, the variance in reading scores in-

creased because higher-scoring students gained more. Unfortunately, the evidence

suggests that the gap has stopped narrowing, and it is unclear if the gap has begun

widening again (Phillips, Crouse, and Ralph 1998). Grissmer, Kirby, Berends, and

Williamson (1994) have shown that Hispanic students have also made gains rela-

tive to whites. There is less evidence, however, of achievement score convergence

by socioeconomic status (Hedges and Nowell 1998).

Average NAEP10 math and reading test scores for 9 year-olds by race and free

lunch eligibility, as a social class indicator, are presented in figures 1.2 and 1.3.

Despite the fact that the political and media rhetoric in recent years has focused

on “failing schools,” the upward trend in math scores continued throughout the

1990’s and into the 21st century. All children made gains over this period, although

Hispanic and black children remain disadvantaged relative to white and Asian

children. These trends also indicate that the Asian advantage over other groups

has deteriorated slightly in recent waves while test scores have generally continued

to rise. Unfortunately the measure of social class available for these data is limited

to eligibility for reduced-price and free lunches and the timeline is based on only

three assessments. Basing trends on timelines this short is difficult, yet the graph

suggests that while math scores continued to grow for children not eligible for free

lunches, mirroring the white trend, math scores were stagnant for disadvantaged

children between 1996 and 2000, but improved significantly in 2003.

The reading scores presented in figure 1.3 bounce around so that distinguishing

trends from sampling fluctuations is a problematic exercise. To the extent that

gains between 1996 and 2002 were real, the upward trend appears to have been

largely derailed during the latest wave for all groups except Hispanic children.

More than assessing trends in the data, figures 1.2 and 1.3 illustrate that social

10These data are gathered from the online “The Nation’s Report Card,” available athttp://nces.ed.gov/nationsreportcard/naepdata/search.asp.

10

Figure 1.2. NAEP Math Achievement Scores for 9 Year-Olds by Race and Free LunchEligibility (as a Social Class Indicator) for Selected Years

190

200

210

220

230

240

NA

EP

Mat

hem

atic

s

1990 1992 1996 2000 2003Year

Total Not Elligible−−Free Lunch

Reduced Lunch Free Lunch

White Black

Hispanic API

SOURCE: U.S. Department of Education, Institute of Education Sciences, NationalCenter for Education Statistics, National Assessment of Educational Progress(NAEP), 1992, 1994, 1998, 2000, 2002, and 2003 Reading Assessments.

class differences (although poorly measured) are often the magnitude of differ-

ences found across race/ethnic groups. Perhaps because of the social visibility

and salience of race in American society, research often focuses on race differences.

Indeed, social class is often the pre-eminent explanation invoked to explain race

differences, yet social class inequalities within race/ethnic groups are often robust

and difficult to explain quantitatively. As this discussion moves forward, it is im-

portant to recognize that Lareau’s (2003) model, while possibly lending itself to

explaining race differences, is also a potentially important mediator of social class

differences in children’s test scores. Given that social class differences are similar

in magnitude to racial disparities, and appear to track them over time, social class

11

Figure 1.3. NAEP Reading Achievement Scores for 9 Year-Olds by Race and FreeLunch Eligibility (as a Social Class Indicator) for Selected Years

190

200

210

220

230

NA

EP

Rea

ding

1992 1996 2000 2003Year

Total Not Elligible for Free Lunch

Reduced Lunch Free Lunch

White Black

Hispanic API

SOURCE: U.S. Department of Education, Institute of Education Sciences, NationalCenter for Education Statistics, National Assessment of Educational Progress(NAEP), 1992, 1994, 1998, 2000, 2002, and 2003 Reading Assessments.

remains a potent source of differentiation among children in need of explanation.

1.4 Individual-Level Differences in Achievement

While not all studies have found substantial differences in children’s early academic

competencies (see Entwisle, Alexander, and Olson 1997 for inequalities at first

grade entry among Baltimore area students), studies by a number of authors across

numerous data sources report that early differences by race and social class are

pronounced (Lee and Burkam 2002 using the ECLS-K; Applebee, Langer, and

Mullis 1988 using NAEP; Farkas and Beron 2004; Phillips, Brooks-Gunn, Duncan,

12

Klebenov, and Crane 1998 using the CNLSY and IHDP; see also Stipek and Ryan

1997; Hart and Risley 1995 using The Social World of Children Learning to Talk).

The study by psychologists Hart and Risley (1995), which tracked a group of

children from 9 months to 3 years, is uniquely informative. Their study uncovered

vast differences in the linguistic culture of the home by social class (see also Heath

1983; Lareau 1987, 1989, 2003). Where children in professional families heard

2,150 words per hour on average, working-class children heard 1,250, and children

in welfare families heard only 620. The differences over the first three years of

life are staggering: 30 million, 20 million, and 10 million words for children from

professional, working-class, and welfare families, respectively. Children in different

social strata acquire vastly different vocabularies and correspondingly differing

amounts of knowledge about the world, which lead to important pre-kindergarten

differences in cognitive achievement.

In agreement with Hart and Risley’s (1995) findings, Farkas and Beron (2004)

report significant social group differences in children’s oral vocabulary and vocab-

ulary knowledge by 36 months of age (see also Jencks and Phillips 1998; Guo

1998). Lee and Burkham (2002: 1) use the ECLS-K11 to study children’s school

readiness at kindergarten entry, noting, “There are substantial differences by race

and ethnicity in children’s test scores as they begin kindergarten. Before even

entering kindergarten, the average cognitive score of children in the highest SES

group are 60% above the scores of the lowest SES group. Moreover, average math

achievement is 21% lower for blacks than for whites, and 19% lower for Hispanics.”

Race and social class differences in children’s academic skills at kindergarten

entry are marked. Given these large initial differences, how does previous research

suggest these differences change over time? Data drawn from a variety of sources

indicate that children of different social groups grow at different rates throughout

childhood. Using a sample of students from the Baltimore area, Entwisle and

Alexander and their colleagues report that although there are few black-white

differences in reading and math scores in first grade, their paths diverge over

time (Entwisle and Alexander 1992; 1994; Alexander, Entwisle, and Olson 1997,

2001). Low socioeconomic status Baltimore area children also have flatter growth

trajectories (Alexander, Entwisle, and Olson 2001).

11This is the same data used for the analytic sections of this work.

13

Meaningful disparities in the growth of children’s academic competencies are

convincingly documented using nationally representative data as well. Farkas and

Beron (2004) find broad differences in children’ oral vocabulary and vocabulary

knowledge growth throughout early childhood, with strong social group differences

in the assessment when children are younger. However, Farkas and Beron (2004)

find no widening of social group gaps in vocabulary during the school years, which,

as these authors note, may be a result of the more uniform vocabulary used in

school.

Bryk and Raudenbush (1988) observe that children whose mothers have lower

educational attainment perform less well on reading and mathematics tests and also

exhibit lower learning rates. In a similar vein, Boardman and colleagues (2002)

report that the children whose mothers have a low education fall increasingly

behind with age, as do black children. The achievement trajectories among black,

white, and Hispanic students differ in important ways. Racial disparities between

non-Hispanic black and white children increase steadily with age, more strongly

for reading than math scores, but while Hispanic children score lower than whites,

the difference remains constant over time. Boardman et al. (2002) also find that

the children whose mothers have a low education fall increasingly behind with age,

as do black children. The net result is the well known fact that black and Hispanic

and lower social class children acquire fewer reading and mathematics skills as they

age.

Reardon (2003) employs a three-level piecewise growth modeling strategy to

study the growth of reading and mathematics gaps by race and social class over

the kindergarten and first grade years. Although between-school compositional

factors appear to account for a significant portion of racial gaps in first grade

growth rates, out-of-school and within-school processes are strongly implicated in

children’s school readiness, differential growth rates over the summer, and over the

kindergarten year (see also Downey et al. 2004; Fryer and Levitt 2004). According

to Phillips, Crouse, and Ralph (1998a), who used a number of data sources, white

students who begin school with achievement scores at the population mean will

leave high school with scores that are still at the mean, whereas black students

drop significantly in the distribution. The estimated decrease from the mean is

sizeable, .34 standard deviations for math and .39 standard deviations for reading.

14

In addition, they found that (Phillips et al. 1998a: 254) 56% of the math gap and

43% of the reading gap at the end of high school can be attributed to the fact that

blacks begin school with fewer skills than whites.

Many minority children face double jeopardy since the race/ethnicity effects

that have been discussed so far reflect disparities in addition to socioeconomic

disadvantages in cognitive development. Higher poverty rates among black and

Hispanic children are well documented and are a persistent feature of more general

inequality patterns. In many cases, socioeconomic disadvantages are larger than

those documented across racial and ethnic categorizations.

The evidence is clear that children from different social groups begin school

with vastly different reading, mathematics, and general cognitive skills, and, fur-

thermore, that children continue to learn at different rates as they age. Under-

standing how and why is not only important for a theoretical understanding of

developmental processes, but also for decision making educators and policy formu-

lators. The movement to a national policy designed around ‘high stakes testing’

highlights the salience of social group differences in children’s academic compe-

tencies. If schools are to leverage their resources to maximum effect, policy needs

to be based on a clear understanding of developmental processes, within which

children’s family lives are strongly implicated. If the formal schooling system is

to achieve the goals outlined in NCLB of reducing social group achievement gaps,

the education system needs to become more involved in facilitating the learning of

children at younger ages.

1.5 Social Group Differences: Perspectives

Before beginning the more detailed discussion of Lareau (2003) and work relating

family characteristics to social group distinctions in academic outcomes, it is worth

embedding the family issues that are the core of this research into a broader net-

work of ideas on educational inequality.12 Phillips and colleagues (1998b) note two

competing perspectives employed to inform our thinking about racial differences

12The importance of intergenerational processes that have already been noted strike me ashaving significant implications for life-course studies, although this research is right-truncated atthe far left end of the age distribution whereas most life-course research is left-truncated to theright of center.

15

in mathematics and reading skills. In addition, I elaborate on and generalize these

perspectives to socioeconomic disparities, noting that racial and socioeconomic in-

equalities may be in large measure the product of differing processes, as evinced

by the residual race effects that are typically encountered in regression modeling.13

The first perspective has a developmental focus. According to this perspective,

social group differences in home environments result in differences in children’s

cognitive skill levels at the time they enter elementary school. Schools, therefore,

are not the primary source of social group differences and may not be able to

readily alter disparities. Rather, children in different social groups have recourse

to differing levels of human, cultural, and social capital through their parents, with

robust developmental consequences. Much of the work cited previously supports

this perspective (e.g. Farkas and Beron 2004; Coleman et al. 1966; Lee and

Burkham 2003), although family-based controls may not be sufficient to account

temporal patterns of change once children enter school (e.g. Fryer and Levitt 2004)

There are three primary ways that families under this framework can influence

children’s achievement: (a) Children may start school with baseline differences

that do not change over time. Although cross-sectional data have been shown to

support such a proposition, longitudinal data suggest that not all social group dif-

ferences in achievement are constant with age (e.g. Phillips et al. 1998; Downey

et al. 2004; Alexander, Entwisle, and Olson 2001; Fryer and Levitt 2004; Rear-

don 2003); (b) Because some families are not able to facilitate children’s learning,

disadvantaged children may evince flatter slopes, which means that differences

in child achievement grow over time. Previous studies, unfortunately, have not

been able to address this possibility sufficiently because data with adequate family

controls and school nesting have not been available. To the extent that schools

differentially impact academic growth rates and children are non-randomly allo-

cated to schools (Condron and Roscigno 2003), previous estimates are an unknown

admixture of between-child and between-school effects (see Raudenbush and Bryk

2002: 137, figure 5.1); (c) Children may learn the same amount in the school year,

but differential growth rates over the summer when students are out of school may

contribute to social group disparities (Entwisle and Alexander 1992, 1994; Alexan-

13Notably, Fryer and Levitt (2004), using the ECLS-K, largely explain black-white gaps inreading and mathematics achievement using only a small covariate list, suggesting that, at leastfor recent cohorts of children, black-white disparities are largely class and resource based.

16

der, Entwisle, and Olson 1997, 2001; Heynes 1978). In addition, (b) and (c) may

be combined, suggesting family influences throughout the year.

The second perspective noted by Phillips and colleagues (1998) comes in “strong”

and “weak” forms and is rooted in structural explanations for social group differ-

ences in children’s cognitive development.14 The general argument of this per-

spective is that neither initial skill inequalities nor home environments account for

cognitive skill differentials as children age. The “strong” version contends that

children start out with similar cognitive skills, but achievement disparities arise

when children’s teachers either ask too little of them, teachers are incompetent,

teachers are classist or racist, or other features of the school produce dispropor-

tionate benefits or detriments for one group over another. However, data from the

ECLS-K suggest that social group differences are already in place by the time chil-

dren enter kindergarten (Denton et al. 2000; Lee and Burkham 2002), and other

researchers have noted that schools may have a compensatory rather than negative

effect on children’s learning (Alexander, Entwisle, and Olson 1997; Downey et al.

200415). The “weak” version recognizes that students may enter school with differ-

ent skills, but that the gap widens as a result of schooling, which is consistent with

Fryer and Levitt’s (2004) and Reardon’s (2003) findings. Both the “strong” and

“weak” versions posit that social group differences in growth rates are principally

a function of school or classroom characteristics.

Of course, student differences may change over time due to some combination of

school and family influences. For example, students from homes where the parents

have fewer resources may exhibit lower growth rates, either during the school year,

over the summer, or both, and because their parents have low human and financial

capital they may also attend poor schools that are less able to foster achievement.

These students could face a double disadvantage in the case of lower socioeconomic

status, and a triple-disadvantage in the case of poor and minority children. How-

ever, data from Baltimore suggests that schools may have a compensatory rather

than a negative effect, at least for reading skill development (Entwisle and Alexan-

14The ‘reproductionist’ perspective on the role of schools in achievement gaps is drawn froman entirely different ideological perspective than NCLB and is largely inconsistent in the de-tails, although there may be apparent global similarities, i.e., schools are the principal culprit inachievement gaps.

15Black children during the kindergarten and first grade years being a key exception.

17

der 1994), which suggests that the achievement patterns evinced by children may

be a complex pattern of school and family influences, and that school influences

may be more complex than structural explanations for social group disadvantages

have suggested. As a study of the parenting strategies outlined by Lareau (2003),

the research presented here is concerned principally with the first perspective, al-

though the methodology employed allows the model to account for constant school

level relationships by comparing children in the same schools and estimating the

average within-school association (which, unfortunately, Downey et al. 2004 did

not do). In addition, the flexibility of the growth modeling approach utilized here

allows children’s growth to capture any of the above forms, although the models

used are not specified to test all of these many hypotheses.16

1.6 The Contribution

Before previewing the following chapters, it is worth taking a moment to note how

this work fits in with the larger literature from which it draws. This work makes

a number of important contributions to the existing literature on children’s family

lives and academic achievement. First, by addressing Lareau’s (2003) important

ethnographic piece17 with a different methodology, my research tests influential

findings that have been studied at the micro level, quantifying relationships and

assessing the feasibility of generalization to the U.S. population. Indeed, there

is a hierarchy of detail gathered across ethnographic research, smaller-scale de-

tailed psychological data sets, and the large scale survey enterprizes such as the

one leveraged here. Finding correspondence between ethnographic and survey re-

search offers insights into how the ethnographic research generalizes to the larger

population on the one hand, and provides insights into processes that are not

16The proximity of the family to developmental processes means that not all of these competinghypotheses need to be tested. If this were a study of school-effects, however, eliminating family-level selection processes would be a principal concern.

17This important work has received an impressive number of accolades, including: (1) AESACritics Choice Award, American Educational Studies Association. (2) C. Wright Mills AwardFinalist, Society for the Study of Social Problems, (3) Sociology of Culture Section Best BookAward, American Sociological Association, (4) William J. Goode Best Book Length Contibu-tion to Family Sociology Award, American Sociological Association, and (5) 2004 DistinguishedContribution to Scholarship Award, American Sociological Association Section on Children andYouth.

18

discernable in statistical analyses on the other.

Second, by elaborating key mediating mechanisms in social class and racial

disparities, this dissertation seeks to further clarify the pathways through which

social class is transferred intergenerationally. Indeed, because both race and social

class are implicated in so many ways over the life course, research highlighting the

development of race and class differences provides key details on group biographies

generally not observable when older samples are relied upon. Studies of adults,

for example, generally have little to say about the processes by which social group

disparities are reproduced, although they may control for retrospectively measured

covariates.

Third, little research has been conducted on children making the transition to

school using nationally representative, longitudinal data. This research elaborates

on the relationship between family environments and children’s academic growth

with a detail that has not been possible using prior data. Understanding the

family-achievement relationship when children are in school is important for policy

makers seeking to ameliorate race and class differences in educational outcomes. If

educational policies are to succeed, they must be designed with the whole gestalt

of children’s lives in view. The family-child dimension of academic achievement is

one piece of that puzzle, but one of profound significance. Hopefully, illustrating

family processes important for children’s scholastic growth will help policy makers

consider new ways to capitalize on the family as an important force in children’s

development. Capitalizing on these linkages is of great consequence when money

for policies is limited and every dollar spent must be spent to effect.

Finally, because children are assessed at the fall and springtime of kindergarten

and first grade, key hypotheses relating family environments to summer increments

and decrements can be assessed (see Cooper, Ni, and Charlston 1996). Previous

research has found evidence of race and class differences in children’s learning

rates when school is out. Clarifying the seasonal nature of learning is important

theoretically and has important policy implications as well.

19

1.7 Chapter Previews

Global differences in children’s growth have been discussed in some detail above.

In Chapter 2, I introduce Lareau’s (2003) concept of ‘concerted cultivation’ in

more detail, relating the notion of ‘cultural capital’ to prior work on children’s

academic achievement. The chapter ends with an outline of the research questions

guiding the analysis. Chapter 3 introduces the data and methods used to conduct

the subsequent analyses.

Chapter 4, the first analytic chapter, addresses the question of whether or not

concerted cultivation can be identified with extant survey data using non-linear

structural equation modeling techniques. In short, there is evidence that concerted

cultivation exists at the population level and that it is strongly associated with

social class as Lareau (2003) suggests, although there are important race differences

net of social class.

Chapters 5, 6, and 7 relate the measure of concerted cultivation to children’s

general knowledge, mathematics, and reading achievement growth, respectively. In

all cases, concerted cultivation is an important predictor of children’s school readi-

ness, in addition to playing a key role in mediating early social group disparities.

The largest reductions in coefficient magnitude occur for the black- and Hispanic-

white reading differences at kindergarten entry, with the black-white disparity

reduced to non-significance and the Hispanic-white gap reduced by approximately

40%. Over time, however, the black-white gap continues to grow for both the math

and reading tests, even when the full covariate list is included in the model. In

general, however, the largest reduction in coefficient magnitudes for the race and

social class indicators occurs when concerted cultivation is added the model. The

typical reduction across growth parameters and tests is between 20–40%, indicat-

ing on the one hand an important role for concerted cultivation in achievement

processes, but also that other factors play key roles.

Chapter 8 summarizes the research questions and points of agreement and

disagreement with Lareau (2003) and other evidence from the literature on early

childhood academic achievement. The second part of Chapter 8 attempts to draw

the gestalt of the earlier chapters into focus, integrating the results and discussing

future research avenues, limitations, and policy implications.

CHAPTER

TWO

Concerted Cultivation

Professionals who work with children, such as teachers, doctors, andcounselors, generally agree about how children should be raised... Thesestandards include the importance of talking with children, developingtheir educational interests, and playing an active role in their school-ing. Similarly, parenting guidelines typically stress the importance ofreasoning with children and teaching them to solve problems throughnegotiation rather than with physical force. Because these guidelinesare so generally accepted, and because they focus on a set of practicesconcerning how parents should raise children, they form a dominantset of cultural repertoires about how children should be raised. Thiswidespread agreement among professionals about the broad principalsfor child rearing permeates our society (Lareau 2003: 4).

2.1 Introduction

While professional’s may agree on the proper way to raise children, it is safe to

say that there is considerable heterogeneity in the U.S. population regarding par-

enting strategies. The idea that the behaviors and actions of parents enacting

these strategies are highly correlated and perhaps even stratified is of consider-

able interest. The notion, restated, is that the diversity in parenting strategies

is not simply deviations around a single overarching strategy, but is composed of

variability around multiple underlying strategies. This is, essentially, the position

taken by Lareau (2002, 2003) in her recent book on childhood and family life. Re-

21

searchers, generally constituting a stratum of individuals immediately implicated

in the quote beginning this chapter, are privy to knowledge of the forms generally

endorsed by child professionals—often implicitly and without overt rationalization,

as are other educated, professional parents. Not all individuals, however, are fully

knowledgeable of what Lareau (2003) describes above as the overarching, dom-

inant child-rearing ideology (see Chin and Phillips 2004 for a counter position).

And even in cases where parents are knowledgeable of middle-class norms, they

may raise their children from what is, ultimately, a meaningfully different script.

The cultural repertoires that parents adopt are often implicit, being more a

product of socialization than explicitly conscious, rational construction. Lareau

(2003) finds that not only do parents employ different strategies, but that the ‘plans

of action’ employed by parents vary importantly by social class, each embodying

their own cultural logic. This work is important because it goes beyond simply

detailing a list of potential covariates related to children’s home environments

by identifying a series of behaviors and actions that reflect parents’ underlying

strategies, which is a notion of sociological salience—given, in particular, the often

taken-for-grantedness of these strategies within social groups (Lareau 2003; see

also Kohn 1977).

Lareau’s research is part of a longer tradition which has noted meaningful class-

based orientations towards parenting strategies (e.g. Kohn 1977; Alwin 1984; the

works in Kohn and Schooler 1983). Being heavily influenced by Pierre Bourdieu

(1977a, 1977b, 1984, 1986; Bourdieu and Passeron 1977), Lareau fits into a broader

conceptual matrix seeking to both elaborate the notion of capital (see Farkas 1996,

for example) and to relate the various dimensions of capital to status attainment

processes. The key idea upon which Lareau draws in framing her discussion of

parenting strategies is the notion of cultural capital, which fundamentally operates1

at a level between the well established micro-level human capital paradigm highly

developed in economics and social capital (Coleman 1988), which has important

macro connotations (Portes 1988, 1998, 2000). In socialization processes, cultural

capital is, on the one hand, a means of transferring human capital from parents

to offspring, and, on the other, a means of generating social capital, connecting

1Although cultural capital is a macro level construct varying across social groups, such associal class, as a form of action, it is a quality of individuals.

22

children to a broader network of relationships. Cultural capital is a deep concept,

not only bridging the divide between human and social capital, but with important

implications for the way individuals come to see society and their place and roles

within it (Lareau 2002, 2003).

Academic achievement is but one of many dimensions of children’s lives within

which, via parenting strategies, cultural capital is implicated. Yet the development

of cognitive skills inherent in academic achievement processes is an important

dimension with consequences important in the development of individual biography

by opening up or limiting educational and labor market opportunities (Farkas

2003; Herrnstein and Murray 1994; Murnane et al. 2000; Kerckhoff et al. 2001;

Raudenbush and Kasim 1998). The remainder of this chapter explores these issues

in more depth.

2.2 ‘Concerted Cultivation’

In Unequal Childhoods: Class, Race, and Family Life, Lareau (2003) reports differ-

ing parental expectations on their role in facilitating their children’s cognitive and

noncognitive competencies. These differing self-oriented expectations have impor-

tant consequences for the organization of children’s routines, which, in turn, have

consequences that ripple through children’s lives. These ripples, which are prod-

ucts of parental cultural resources, have powerful impacts on children’s life chances,

setting them on paths to academic success in some cases, and potential failure in

others. Parenting practices are the pathway through which the social stratifica-

tion of family life and childrearing coalesce to produce persistent inequalities in

educational experiences among and across diverse social groups.

Higher class parents engage in what Lareau terms ‘concerted cultivation’ in

deliberate attempts to foster their children’s cognitive and social skills. Less ad-

vantaged parents, in contrast, engaged in a collection of practices she termed ‘the

accomplishment of natural growth.’2 Both cultural repertoires represent parenting

strategies that embody notions of cultural capital as reflected in parents’ skills,

2I will often refer to simply ‘concerted cultivation’ rather than juxtaposing both concepts. Inaddition, I will at times refer to the ‘concerted cultivation paradigm’ when referring to Lareau’s(2003) model.

23

habits, and styles (see Swidler 1986; Farkas 1990; 1996). Within an intergenera-

tional framework, it is these parenting behaviors through which parents transmit

their human and social capital to their children, perpetuating advantage and disad-

vantage across generations and preparing children for life as members of particular

social classes (Kohn 1977).

Concerted cultivation is a concept capturing a goal-oriented series of prac-

tices parents adopt in order to prepare their children for immediate academic and

long-term labor market success. Higher social class parents in Lareau’s study

viewed concerted cultivation as their primary responsibility and, accordingly, par-

ents largely structured their lives in efforts to directly stimulate child development.

According to Lareau (2003, p. 238):

In these families, parents actively fostered and assessed their children’stalents, opinions, and skills. They scheduled their children for activi-ties. They reasoned with them. They hovered over them and outsidethe home they did not hesitate to intervene on the children’s behalf.They made a deliberate and sustained effort to stimulate children’sdevelopment and to cultivate their cognitive and social skills.

The process of concerted cultivation was one of continual engagement, whether

through the structuring of children’s time with formal activities, or by means of

language use designed to elicit responses from children, or interfacing with other

professionals on their children’s behalf.

Lower class parents generally perceived themselves as caretakers, providing

material resources and comforts to their children, often in the face of financial

difficulties.

The working-class and poor parents viewed children’s development asunfolding spontaneously, as long as they were provided with comfort,food, shelter, and other basic support. I have called this cultural logicof child rearing the accomplishment of natural growth... Parents whorelied on natural growth generally organized their children’s lives sothey spent time in and around home, in informal play with peers, sib-lings, and cousins... Unlike in middle-class families, adult-organizedactivities were uncommon. Instead of the relentless focus on reason-ing and negotiation that took place in middle-class families, there wasless speech (including less whining and badgering) in working-classand poor homes. Boundaries between adults and children were clearlymarked (Lareau 2003, pg 238)....

24

These parents clearly delineated the world of adults from the world of children, and

where middle and upper middle class parents expended great effort to structure the

lives of their children, largely relying on formal activities such as sports teams or

dance lessons in which children interact repeatedly with adults, lower social class

parents gave their children much greater freedom to structure their own activities.

Correspondingly, lower social class parents were much less involved in structuring

their children’s lives and were far less oriented in managing and encouraging their

children’s skill development.

These disparate strategies lead to different childhood experiences and orienta-

tions and are consistent with previous research on social class differences in par-

enting (Kohn 1977; Kohn and Schooler 1969; Kohn 1959; Duvall 1946; Lynd and

Lynd 1929). Table 2.1 presents a summary table from Lareau (2003: 31) depicting

the developmental ‘consequences’ of both parenting strategies. In Lareau’s study,

the lifestyle organization and child-parent interaction underlying the class-based

practice of concerted cultivation were continually orchestrated towards their chil-

dren’s development, whether the action was a conscious or unconscious adoption

of the cultural logic underlying the strategy. The distinctions in temporal organi-

zation between middle and lower-class parents, i.e., concerted cultivation and the

accomplishment of natural growth, were pronounced,3 as were the distinctions in

language use (as we will see with more detail below) and interactions with profes-

sionals. Although Lareau (2003: 244) suggests that the differences in experiences

between children raised in homes differing by social class and parenting strategy

should lead to advantages in standardized testing for children from homes practic-

ing concerted cultivation, this model has yet to be directly tested.

2.3 Social Class

Lareau’s (2003) discussion of concerted cultivation is fundamentally a discussion

about social class and the resources available to families of different classes. Among

sociologists and developmental psychologists, discussions regarding resources usu-

ally fall into a more general debate regarding socioeconomic status, and vice versa.

3For example, Lareau (2003: 264) was unable to find a “middle-class child who participatedin no organized activities.”

25

Table 2.1. Typology of Differences in Child Rearing

Child-Rearing Approach

Accomplishment ofConcerted Cultivation Natural Growth

Key Elements Parent actively fosters and Parent cares for child andassesses child’s talents, allows child to growopinions, and skills

Organization Multiple child leisure ac- “Hanging out,” particu-of Daily Life tivities orchestrated larly with kin, by child

by adults

Language Use Reasoning/directives DirectivesChild contestation of Rare questioning or

adult statements challenging of adultsExtended negotiations by child

between parents and General acceptance bychild child of directives

Interventions Criticism and inter- Dependence onin Institutions ventions on behalf institutions

of child Sense of powerlessnessTraining of child to take and frustration

on this role Conflict between child-rearing practices athome and at school

Consequences Emerging sense of entitle- Emerging sense of con-ment on the part of the straint on the part ofchild the child

Source: Leareau (2003), pg. 31.

Citing Mueller and Parcel (1981), McLoyd (1998: 188) defines socioeconomic sta-

tus as “an individual’s, a family’s, or a group’s ranking on a hierarchy according

to its access to or control over some combination of valued commodities such

as wealth, power, and social status.” Socioeconomic status is a (potentially mul-

tidimensional) latent construct,4 and so it is measured in various ways, gener-

4This type of latent construct is formative, as compared to reflective, that latter being thetype of latent variable modeling common in factor analysis and structural equation modeling(Skrondal and Rabe-Hesketh 2004).

26

ally including parental occupation, parental education, family income, and some-

times prestige, and power (McLoyd 1998). From this list of factors, one can see

that socioeconomic status is somewhat more stable than income measures because

parental education, in particular, is mostly a stable characteristic in adulthood; al-

though a parent may lose their job, if there are two parents, measures like parental

occupation may still be quite stable over time, though income itself can change

significantly.

Socioeconomic status, however, is used in the literature to denote individual,

family, or group differences other than simply wealth and power and social status

(Bradley and Corwyn 2002). In many cases it is used to reference differences in

parental skills, and noncognitive traits like parenting styles, and other behaviors

related to concepts like ‘concerted cultivation.’ These discussions operate at two

levels. On the first, the argument is “valueless,” that is, children from different

social classes acquire different skills, habits, and styles. On the second, these skills,

habits, and styles are rewarded differently based upon their similarity to those

of higher socioeconomic status group members who control the reward system

(Lamont and Lareau 1988). In discussions of cognitive achievement, it must be

noted, social class need not operate at the second level in all instances because

differences in skills, habits, and styles, imply real development; doing math, for

example, is not highly subjective and so is not subject to class biases in assessment,

and socioeconomic differences can produce outcome differences on these measures

(of course, the tests are probably not perfectly objective either).

While I will refer to socioeconomic status, or class, it is an imprecise language

for discussing children’s developmental trajectories on its own because it is ulti-

mately an agglomeration of characteristics, including its own mediating pathways.

To be more precise, socioeconomic status is a very general construct where the vari-

ables of interest are often implicit rather than explicit, a characteristic that makes

works like Lareau’s (2003), which is principally about the underlying mechanisms

of social class, insightful and important. The idea of human capital, developed by

economists (Shultz 1961, 1963; Becker 1964), and its more social derivations, cul-

tural (e.g. Lamont and Lareau 1988) and social capital (Coleman 1998), are useful

tools for specifying the family and social processes that are thought to produce

racial and socioeconomic differences in child developmental outcomes like academic

27

achievement.

2.4 Resources

At the beginning of a child’s life, the family, as the principle agent of protection and

socialization, establishes and catalyzes the child’s early cognitive development. Not

all families are able to provide equally for their youngsters, however. Some families

have a paucity of resources. These resources may be of different types: financial,

human, cultural, and social. Often, a deficit in one area implies a deficit in other

areas. In observing strong social class distinctions in parenting strategies, Lareau

(2003) observed distinctions in financial, human, and social capital resources.5 It

is this collection of resources that researchers typically refer to when they refer to

social class, and, within the larger discussion of ‘resources,’ concerted cultivation

represents an explanatory pathway through which these resources influence child

academic achievement.

2.4.1 Financial Capital

Financial capital has been found to be an important predictor of child outcomes,

although it seems that financial resources proxy for underlying class-based behav-

ioral patterns like concerted cultivation in many circumstances. Family income

appears to be more strongly related to children’s ability and achievement than

emotional outcomes (Brooks-Gunn and Duncan 1997). Economic deprivation may

be most harmful to children’s life chances when it occurs early in life (Duncan

and Brooks-Gunn 2000), and persistent poverty has more adverse effects than

transitory poverty on preschool children’s cognitive development. Although the

association between the timing of poverty and children’s cognitive achievement is

not entirely clear (Smith, Brooks-Gunn and Klebanov 1997), timing appears to

become more important as children age into adolescence (Guo 1998). In general,

5Social capital deficits in Lareau’s (2003) study were somewhat more ambiguous by socialclass than for financial and human capital. While higher social class parents had greater accessto educational knowledge through parental networks, these families appeared to be less inte-grated with their local neighborhoods and extended-family members. This, to a certain extent,highlights the multidimensional nature of social capital. For a more thorough discussion, seeHorvat, Weininger, and Lareau (2003).

28

however, children who experience depressed economic situations, whether they be

of long or short duration, do not do as well as children who do not experience

poverty (McLoyd 1998).

Financial resources are important for child development by determining access

to academic resources, quality institutions such as better schools, in addition to

providing the basic necessities of life like adequate nutrition. Guo and Stearns

(2002), for example, note that parents with more resources, by providing numer-

ous types of cognitively stimulating materials, allow their children to choose the

resources they enjoy most, increasing the likelihood they will dedicate more time

to those materials.6 Indeed, access to material resources provides opportunities

for young children not only to play with the stimulating materials that they enjoy,

but as Lereau (2003) observed, financial resources also create opportunities for

experiences like participation in structured activities (e.g. soccer club), which is

so important in the cultural capital paradigm.

Chin and Phillips (2004) raise the question of whether or not differences in

observed levels of concerted cultivation are fundamentally the product of differing

levels of financial resources available to parents. They note that many parents

in their ethnographic study are familiar with the cultural logic of concerted cul-

tivation but are unable to acquire the goods and services to translate their goals

and desires into action. Lareau (2002: 771-772) also discusses the role of financial

resources, noting that, “[i]t is the interweaving of life experiences and resources,

including parents’ economic resources, occupational conditions, and educational

backgrounds, that appears to be most important in leading middle-class parents

to engage in concerted cultivation...”

Middle-class parents often were preoccupied with the pleasures andchallenges of their work lives. They tended to view childhood as a dualopportunity: a chance for play and for developing talents and skills ofvalue later in life...

Working-class and poor parents’ conceptions of adulthood and child-hood also appeared to be closely connected to their lived experiences.For the working class, it was the deadening quality of work and thepress of economic shortages that defined their experience of adulthoodand influenced their vision of childhood. It was dependence on pub-lic assistance and severe economic shortages that most shaped poor

6They discuss this in the context of environmental-genetic interactions.

29

parents’ views. Families in both classes had many worries about basicissues: food shortages, limited access to healthcare, physical safety, un-reliable transportation, insufficient clothing. Thinking back over theirchildhoods, these parents remembered hardship but also recalled timeswithout the anxieties they now faced. Many appeared to want theirown youngsters to concentrate on being happy and relaxed, keepingthe burdens of life at bay until they were older (Lareau 2002: 771).

Financial resources, on the one hand, have lasting implications for parental views

on child-rearing and the ability for parents to realize these views, and on the

other, do not sufficiently explain social group gaps in achievement (Mayer 1997).

For this reason, it is necessary to expand the notion of ‘capital’ so that it embodies

characteristics of individuals and families that in many cases are more proximate

‘causes’ of childhood academic achievement processes.

2.4.2 Human Capital

Coleman (1988) considered human capital to be the most original and important

development in the economics of education. Farkas (1996: 9), in a similar vein,

begins by noting the essentially complete triumph and broad applicability of the

concept. The basic idea of human capital is that ”[j]ust as physical capital is

created by changes in materials to form tools that facilitate production, human

capital is created by changes in persons that bring about skills and capabilities

that make them able to act in new ways” (Coleman 1988: 100). Human capi-

tal is an individual characteristic since it resides within people. Thus, children’s

achievement trajectories are the rate of human capital development, and changes

in reading and math skills imply changes in individual productive capacities. Of

course, human capital embodies more than simply reading and math skills, and

to some extent may be specific to the outcome of interest (for example, skilled

craftsman and mathematics professors have differing levels and kinds of human

capital). Like socioeconomic status, it is a latent construct and cannot be mea-

sured directly, although parental cognitive skill and education are two common and

important indicators for studies of children’s academic achievement. However, the

definition of human capital does not immediately imply how parents foster their

children’s human capital development. In this sense, human capital is no more

30

transparent than the more general notion of ‘socioeconomic status’ in elaborating

the processes surrounding children’s skill acquisitions.

The human capital perspective is derived from an intellectual root quite dif-

ferent from sociologists’ concern for socioeconomic status, although in current

thinking these two factors are inextricably intertwined. Where class position em-

phasizes group membership and the shared understandings that constitute group

culture, the human capital paradigm has traditionally emphasized atomistic indi-

viduals engaged in rational, economizing action, “based upon their exogenously

given tastes and endowments of biologically determined skills and abilities. Con-

sequently, group differentials in schooling and training attainment, and in conse-

quent outcomes such as earnings, must be attributable to essentially ‘accidental’

group differences in such tastes and natural abilities” (Farkas 1996: 9). The ra-

tional choice perspective within which the notion of human capital was developed,

particularly as traditionally espoused by human capital economists, ignores the

embeddedness of individual economic action in culture and social structure (Gra-

novetter 1985). Thus, while human capital is an important idea, it is limited and

must be extended both for conceptual reasons and for lack of transparency when

considering intergenerational processes.

2.4.3 Cultural Capital

Tied-up with disparities in financial and human resources, Lareau (2003) and her

team witnessed a further stratification in the cultural logic underlying parental

behavior and actions regarding their children that transcend bank accounts and

endowments of personal productive capacity. Farkas (1996: 10; see also Hodson

and Kaufmann 1982 [cited in Lamont and Lareau 1988]) notes that “on the one

hand, human capital economists offer no realistic understanding of what appears

to be extremely different educational ‘investment’ by poor and minority groups.

That is, the economists’ emphasis on individual choice is blind to patterns of cul-

tural resources and influence as well as choices that are made for the individual by

other persons.” Human capital and rational choice need a mechanism for social re-

production other than exogenous tastes and preferences since neither is randomly

distributed in the population. To be more specific, correlations within-groups over

31

time imply that although tastes and preferences may be exogenous influences on

young children, they are endogenous to the group, so eventually offspring repro-

duce these social or cultural forms. However, the individual, rational choice aspect

of social reproduction means that these forms are never perfectly reproduced. Re-

ferring to the Farkas cite at the opening of this paragraph, for child development

a perspective reconciling individual action with group dynamics that translates

human capital intergenerationally is needed.

Cultural capital has been posited as that mechanism, or series of mechanisms.

This perspective is rooted in the writings of Bourdieu (1977a, 1977b, 1984, 1986;

Bourdieu and Passeron 1977), namely the concepts of cultural capital and habi-

tus. Two difficulties immediately present themselves to researchers interested in

‘cultural capital.’ First, Bourdieu’s own writings lack clarity, giving rise to inter-

pretational difficulties (Lamont and Lareau 1988; Kingston 2001). Second, Bour-

dieu’s conceptualization was based on the context in France, suggesting that his

vision of ‘cultural capital’ does not have the same meaning in the United States.

Importantly, Kingston (2001: 91) notes, when discussing Lamont’s (1992) U.S.

based study regarding “the symbolic boundaries that upper-middle-class men in-

voked to separate themselves from others and, reciprocally, to define their own

sense of group identity. In establishing their own symbolic boundaries, these men

expressed little concern about cultural orientations—decidedly less than they did

about moral and economic considerations,” which stands somewhat in contrast to

France.

In response to these ‘difficulties,’ cultural capital has been expressed in a num-

ber of ways, some arguably more consonant with Bourdieu’s original intent, others

more ‘Americanized.’7 Lamont and Lareau (1988: 156), for example, defined cul-

tural capital as “institutionalized, i.e., widely shared, high-status cultural signals

(attitudes, preferences, formal knowledge, behaviors, goals, and credentials) used

for social and cultural exclusion.” This widely regarded definition in the education

literature is notable for highlighting the exclusionary and often arbitrary nature

of cultural signals. Social groups have their own distinctive subcultures that are

transmitted to their offspring. These subcultures are signalled through actions,

i.e., habits and styles, with within- and between-group implications. According to

7Referring to the United States.

32

this interpretation, cultural capital is a signaling mechanism that is rewarded by

educators when the appropriate forms are followed. That is, high or middle-class

cultural forms are reified by teachers and school administers, while the forms char-

acterizing lower class and disadvantaged groups are denigrated. McLoyd (1998:

193), for example, has argued that “teachers tend to perceive poor and low-SES

students less positively and to have lower achievement expectations than for non-

poor children, largely on the basis of noncognitive considerations (e.g., speech

patterns and dress).” This implies that the important roles that parents play in

facilitating their children’s education operates, at least in large measure, by influ-

encing children’s development of social styles and ability to signal class position,

rather than directly through actual knowledge acquisition and cognitive skills.

In another ethnographic study, Lareau (1989) pointed to differences in parental

interaction with schools by socioeconomic status as an important mediator of chil-

dren’s schooling experiences. Higher class parents were more comfortable with

teachers and were more likely to intercede on their children’s behalf. Lower class

parents, on the other hand, were largely disengaged from their children’s teacher

and the school. These different behaviors not only prepared children differently

to interact with teachers, but also played a role in defining teacher-student rela-

tionships. There are also important differences in parenting practices that may

play an important role in miscommunication been teachers and their young stu-

dents of different social classes. According to Heath (1983), lower-class and black

parents engage their children with different speech patterns than middle-class par-

ents. Lower-class and black parents use restricted codes and are more likely to

use directives (“Don’t do that.”) whereas middleclass parents use elaborated codes

that draw children into conversation (“Why are you doing that? Could you stop

please?”). Lareau (1989; 2003) has discussed similar patterns (see also Hart and

Risley 1995) and noted that parents applied discipline differently to children by

socioeconomic status (Lareau 2002, 2003; Kohn 1977). These differing styles of

speech and punishment patterns may result in the behavioral difficulties more of-

ten found among lower class than upper class students because children whose

parents use directives and physical punishment may misunderstand the serious-

ness of their teacher’s warnings regarding their behavior since they come to expect

anger to be expressed with directives and physical punishment threatened when

33

their parents finally “get serious” with them. Middle-class students, on the other

hand, are raised with a different set of expectations that are more consonant with

their teacher’s expectations. For instance, rather than giving directives, questions

are asked (“Would you please sit still?”), and physical punishment or its threat

are anathema in the classroom.

So far, the preceding cultural capital discussion suggests that social differences

in child academic outcomes result from the parent-teacher/school interactions be-

cause the middle-class educational culture does not accommodate disadvantaged

children’s non-cognitive behaviors. However, as other researchers have noted, cul-

tural capital is more than a series of signals (see Kingston 2001 for a discussion); it

directly produces tangible skills by transmitting styles and behaviors that are more

than simply class-based signals (Swidler 1986; Farkas 1990; 1996). From this per-

spective, which it can be argued is more ‘productivity based’ along a U.S. model,

the “general cultural background, knowledge, disposition, and skills” (MacLeod

1987: 13) of cultural capital are emphasized–which I have referred to previously

as skills, habits and styles, following Farkas (1996). Embedding this democratized

version of cultural capital into the previous discussion (of Heath 1983, Lareau

1987, 1989, and Hart and Risley 1995) for instance, suggests that the middle-

class speech patterns with which children are raised encourages children to think

critically, justify their actions, and requires them to think about their actions in

relation to those around them; these speech patterns are one of the hallmarks of

‘concerted cultivation’ and represent a potentially important means of cognitive

skill acquisition for children.

Embodied in the disparate, class-based speech patterns of concerted cultivation

and the accomplishment of natural growth are skills valuable in the educational

enterprise. This aspect of cultural capital conforms more with a developmental

perspective that views child development as an outgrowth of family processes,

parenting styles, and environment, with real implications for children’s human and

cultural capital formation, and not simply the result of values placed on the skills

by the dominant class and its gatekeepers.8 In addition, there is an ‘impersonality’

involved in standardized testing, so while teachers may downgrade students who

8Of course, the signaling component of concerted cultivation may take on renewed importanceat the extreme right tail of the social class distribution (e.g. Mills 1956).

34

‘miss-signal,’ it is less clear how signalling mechanisms could bias more objective

test scores downward, particularly when skill differences are found so early in

children’s lives (see Chapter 1). The early skill differences are important for the

skill-oriented perspective because one could argue that teacher encouragement (e.g.

the McLoyd 1998 cite earlier) and tracking via a class-based selection method biases

standardized test scores downward. While this may still occur, early childhood

differentials clearly suggest that the skill-based component is much larger, at least

at kindergarten entry.

In all likelihood, cultural capital in American society is not merely a signalling

mechanism or simply a concept adequately (or necessarily meaningfully) opera-

tionalized as highbrow culture, a la Bourdieu (see for example DiMaggio 1982;

DiMaggio and Mohr 1985). Within this Americanized tradition, Lareau’s (2003)

discussion9 of concerted cultivation posits that parenting strategies lead to cog-

nitive skill differences (at least as measured by standardized tests) that are the

results of different environments and experiences that parents create for their chil-

dren based upon the cultural logic (i.e., cultural capital) of their preferred parent-

ing strategy in addition to proficiency in signaling competencies to professionals.

These skill differences represent a significant class-based advantage because parents

by social class utilize parenting strategies based upon dissimilar cultural logics.

Quantitative research demonstrates that social class indictors such as family

income and the mother’s education may largely operate through parental skills

and habits by shaping the home environment (Guo and Harris 2000; Mayer 1997;

Brooks-Gunn, Klebanov, and Duncan 1996; Smith, Brooks-Gunn and Klebanov

1997). Furthermore, indicators of socioeconomic status have been associated with

parenting styles that have indirect effects on achievement through home skill-

building activities and school behavior (DeGarmo, Forgatch, and Martinez, Jr.

1999). Bradley and Corwyn (2002) also find that higher socioeconomic parents

engage children in more conversations, read to their children more, and provide

more teaching experiences. Their conversations are richer, contain more contingent

responsiveness, include more efforts to elicit child speech, and their teaching style

includes more scaffolding and complex verbal strategies (see also Hart and Risley

9Whereas Lareau’s (1989) earlier work was mostly concerned with the traditional signal-basedconception of cultural capital, her more recent work (2003) appears to have ‘softened,’ allowingfor developmental consequences of parenting strategies as manifestations of cultural capital.

35

1995; Smith, Landry, and Swank 2000).

A key notion that has not yet been defined here is that of ‘habitus,’ which

“is composed of attitudes, beliefs, and experiences of those inhabiting one’s social

world” (MacLeod 1987: 15; See also Dumais 2002). As seen in table 2.1, these

practices further lead to a disparate habitus by social class, resulting in an emerging

sense of entitlement for advantaged children, and what Lareau describes as an

emerging sense of constraint in reference to professionals (e.g. teachers, doctors,

etc.) for lower-class children (see also Kohn 1977; Bowles and Gintis 1976 also

discuss how schools encourage the indoctrination of class-based habitus). The

implication is that concerted cultivation, as a manifestation of cultural capital, is

driven by the habitus of parents, and this habitus is consequently reinforced in

children with the obvious implications for the intergenerational transfer of social

class.

Student Achievement

Numerous studies have attempted to directly operationalize and test notions of cul-

tural capital as high-brow culture (Dumais 2002; Aschaffenburg and Maas 1997;

DeGraaf 1986; Di Maggio 1982; Downey 1995; Ganzeboom, DeGraaf, and Robert

1990) and/or educational resources (Teachman 1987; Blake 1981; Downey 1995;

Burkam, Ready, Lee, and Logerfo 2004). In a paper entitled The Unfulfilled

Promise of Cultural Capital Theory, Kingston10 (2001: 89) argues that “(1) defined

in terms of exclusionary class-related practices and dispositions, cultural capital

does not substantially account for the relationship between social privilege and

academic success and (2) too many conceptually distinct variables have come to

be placed under the big umbrella of cultural capital, creating a distorted sense of

what accounts for academic success.” As Dumais (2002: 49) further notes, “there

is no consensus on what cultural capital means, whether it has an effect, or what

the effect is.” In addition, most studies of cultural capital deal with adolescents,

rather than children. One notable exception using the ECLS-K found few effects

and that, furthermore, the measures of cultural capital did not explain social class

differences in young children’s learning rates over the summertime (Burkam et al.

2004).

10Lareau begins her 2002 piece addressing Kingston (2002).

36

As Lareau’s (2003) work is relatively recent, no quantitative studies have yet

attempted to operationalize cultural capital as a concept such as ‘concerted culti-

vation.’ Given the clarity of Lareau’s research, this sort of conceptual reorientation

represents an a potentially important improvement over previous research for two

important reasons. First, Lareau’s (2003) work is observation-based and not sim-

ply derived from the available covariate list in survey data. Second, given the

broad range of measures researchers have utilized in attempting to operationalize

cultural capital (e.g. Kingston 2001), concerted cultivation is a potentially unify-

ing, theoretically justified construct. Furthermore, there is a large body of research

on children’s achievement that provides a significant amount of indirect evidence

consistent with the concerted cultivation paradigm.

Bradley, Corwyn, McAdoo, and Coll (2001a) use the NLSY HOME inventories

(Bradley 1985) to document significant and meaningful differences in the cultural

capital available in the home environments of children by race and socioeconomic

standing in a way largely consistent with that described in Lareau’s (2003) dis-

cussion of concerted cultivation. Black11 and Hispanic mothers respond less often

to their children, and across all ethnic groups and age groups, non-poor mothers

are more likely to show both verbal and physical affection, and are more likely to

respond verbally. Less affluent parents are also more likely to spank their chil-

dren. Black and Hispanic parents read to their children less often than white and

Asian parents, poor mothers are much less likely to read to their children prior

to school entry, and non-poor mothers are twice as likely to read to their children

three or more times a week. There are also race and socioeconomic differences

in the extent of father involvement with children. When Bradley and colleagues

(2001b) assessed the role of the home environment on early motor and social devel-

opment, vocabulary development, academic achievement, and behavior problems,

consistent relations with the learning environment were found, while parental re-

sponsiveness and spanking varied as function of the outcome, age, ethnicity, and

poverty status. Importantly, Lareau (2003) observed differing distributions of these

various sources of influence by parenting strategy.

A significant portion of the race and socioeconomic differentials in cognitive test

11It should be noted that Lareau (2003: Appendix A) finds few differences by race, but itshould further be noted that she had a very difficult time recruiting middle-class blacks into thestudy.

37

scores have been explained in a way consistent with the concerted cultivation par-

adigm using variables detailing family environments (Parcel and Menaghan 1994;

Duncan, Brooks-Gunn and Klebanov 1994; Brooks-Gunn, Klebanov and Dun-

can 1996; Smith, Broods-Gunn, and Klebanov 1997). Using data from the Infant

Health and Development Program (IHDP), Brooks-Gunn and colleagues (1996) re-

port that black children scored approximately one standard deviation lower than

white children on a measure of IQ, but accounting for ethnic differences in poverty

reduced the race coefficient by more than half. Including differences in the home

environment like the amount of cognitive stimulation, a goal consistently enter-

tained by parents practicing concerted cultivation, reduced the ethnic differentials

by an additional 28%, and explained the influence of mother verbal ability and ed-

ucation. Phillips, Brooks-Gunn, Duncan, Klebanov, and Crane (1998) used both

the NLSY and IHDP to document that although socioeconomic status accounts for

a good proportion of the race gap, including measures of the cognitive environment

attenuates the race coefficient by more than half, and decreases the magnitude of

socioeconomic effects. The IHDP results further suggest that better parenting

measures might explain even more of the gap.

Utilizing the NLSY, Guo and Harris (2000) fully mediate the influence of family

poverty on children’s intellectual development using measures of cognitive stimula-

tion in the home, physical environment of the home, and the child’s health at birth.

Even with these controls, however, they are unable to completely erase the black-

white difference across test scores. Importantly, Guo and Harris (2000) also found

that maternal education has indirect effects on intellectual development through

cognitive stimulation, while maternal ability (AFQT) had direct effects, and also

operated through cognitive stimulation and parenting style (see also Farkas and

Beron 2004).

Indicators of socioeconomic status have been associated with better parenting,

which has indirect effects on achievement through home skill-building activities and

school behavior (DeGarmo, Forgatch, and Martinez, Jr. 1999). Mothers frequency

of stimulation is also important (Smith, Landry, and Swank 2000). In a unique

study of children’s vocabulary growth, psychologists Betty Hart and Todd R. Risley

(1995) tracked a group of children from 9 months to 3 years. Their study showed

that in addition to the differences in content mentioned by Heath (1983) and

38

Lareau (1987, 1989, 2003), children in professional families heard 2,150 words per

hour on average, while working-class children heard 1,250, and children in welfare

families heard 620. The differences over the first three years of life are staggering;

30 million, compared to 20 million, compared to 10 million words for children from

professional, working-class, and welfare families, respectively. Children in different

social strata acquire vastly different vocabularies and correspondingly differing

amounts of knowledge about the world, which lead to important pre-kindergarten

differences in cognitive achievement.

Senechal and LeFevre (2002) report that children’s exposure to books is related

to children’s receptive language, and parental involvement is linked to children’s

emergent literacy. Smith, Landry, and Swank (2000) show that mothers verbal

scaffolding (specifying relations between objects, actions, and concepts) is impor-

tant for understanding individual differences in children’s cognitive skills. Bradley

Rock, and Caldwell (1987) find that academic achievement among black children

is related to the responsiveness of parents and the general emotional climate of

the home, growth fostering materials, paternal involvement, and the physical en-

vironment are also related, although more modestly and less consistently. Bradley

and Corwyn (2002) also find that higher socioeconomic parents engage children

in more conversations, read to their children more, and provide more teaching

experiences. Their conversations are richer, contain more contingent responsive-

ness, and include more efforts to elicit child speech. Their teaching style includes

more scaffolding and complex verbal strategies. In another detailed study, Landry,

Smith, Loncar, and Swank (1997) report that parenting behaviors that are sensi-

tive to children’s focus of interest and that do not highly control or restrict their

behaviors predicted greater increases and faster rates of cognitive language and so-

cial development. Children whose mothers used more restrictive styles have lower

growth rates.

The foregoing discussion highlights a number of parental behaviors, such as

language and use of stimulating materials, related to children’s academic compe-

tencies that are reflective of the underlying parenting strategy that Lareau (2003)

termed ‘concerted cultivation.’ Taken together, these results suggest both that

concerted cultivation is an important predictor of academic success and that con-

certed cultivation captures a macro-level concept and is not simply an artifact of

39

the small samples with which Lareau (2003) was working.

2.4.4 Social Capital

Cultural capital12 has had different meanings for different authors: highbrow cul-

ture, socioeconomic signaling mechanism, and more proximate skills, habits, and

styles used to transmit human capital intergenerationally—all of which are cor-

rect. Whereas financial and human capital inhere in the family and individual,

cultural capital, as the product of interactions, exists within individuals, families,

and social relationships, and so it operates at multiple levels. Cultural capital is

the realization of human and financial capital, but it is also a mode of action whose

value lies in interaction with other individuals. Thus, Farkas (1996: 11) notes that

cultural capital “is compatible with Ogbu’s (1974, 1978, 1986) emphasis upon the

negative schooling effects of castelike minority status.” This is the case because

behavior styles are learned both within-families, from parents and siblings, and

in broader social contexts like peer groups at schools. As noted by Wilson (1996:

72), “skills, habits, and styles are often shaped by the frequency at which they are

found their own community” and

ghetto-related behaviors often represent particular cultural adaptationsto the systematic blockage of opportunities in the environment of theinner city and the society as a whole. These adaptations reflect inhabits, skills, and styles, and attitudes [i.e., habitus] that are shapedover time (Farkas and Beron 2001: 9-10).

Community resources and patterns of relationships can influence children’s cog-

nitive and noncognitive development. People use these social relationships instru-

mentally, which denotes another form of capital, social capital (Coleman 1988).

As with cultural capital, social capital has been conceptualized in more than one

way (see Portes 1998, 2000). Commenting on Bourdieu’s writing on forms of cap-

ital, Portes (1998:4) notes, “through social capital, actors can gain direct access

to economic resources” and that “they can increase their cultural capital through

contacts with experts or individuals of refinement.” Portes (1998: 6) goes on to

12Although I do not specifically incorporate social capital into the modeling framework com-prising the analytic chapters of this dissertation, I discuss social capital to complete the discussionon ‘resources.’

40

state that “the consensus is growing in the literature that social capital stands for

the ability of actors to secure benefits by virtue of membership in social networks

or other social structures.” This derives specifically from Coleman’s (1998) original

definition of social capital.

However, the focus on the instrumental nature of social capital limits the ap-

plicability of the idea, and constrains it to behavior that does not seem consonant

with a complex social world. For example, the influence of peers on cultural cap-

ital in Ogbu’s (1974, 1978, 1986) work has non-positive educational consequences

by virtue of the effects on student behaviors, styles, skills, and attitudes, but the

influence is clearly a function of social capital. Students must adapt their behavior

so that they can access their social networks, which implies that social capital is

not simply vertically oriented, as is the case with financial and human capital, but

can operate horizontally as well. This is not really surprising when we think about

it, however, since we imagine that the role and effect of social capital will vary

depending on the outcome. With the simpler, unidirectional definition of capital,

more is more and less is less, which is not always the case with social capital.

Sometimes more is less and less is more because social capital moderates as well

as mediates social processes, which is exemplified by Ogbu’s work.

The oppositional culture example also leads to a second interpretation of so-

cial capital, the community definition (Portes 2000). Portes (2000) notes that

early community-based definitions suffered from circularity of logic and tautologi-

cal statements, but that more recent conceptions have been better specified. The

literature on contextual effects often wants to get at community-based concepts

of social capital. Raudenbush and Sampson (1999) discuss the development of

ecometrics, the ecological analog to psychometric measures. Collective measures

of social cohesion and control are an attempt to measure the emergent influence

of social capital. Whereas the individual conception of social capital is a “sum is

equal to its parts argument,” the collective, community-based conception is a “the

sum is greater than the whole of its parts” argument. Thus, in Ogbu’s work the

influence of social networks on cultural capital is not simply the result of individual-

level relationships, but is also the product of collective organization, or community

effects that influence individual behaviors and subsequently, cognitive and noncog-

nitive development. The process denotes feedback loops since students who do not

41

behave in certain ways will be unable to access the social capital that inheres in

relations among students, but following the proper behavioral forms replicates the

context and fosters the qualities of the social capital that arise within the context

and consequently influences cultural and human capital development in specific

ways.

Social capital can take a variety of forms, from the acquisition of information

through friendship networks, to implicit collective monitoring of the young based

upon reciprocal relationships, to the formation of formalized institutional arrange-

ments. Zhou and Bankston (1998) in a study of a Vietnamese community in New

Orleans provide a rich account of a community with an impressive degree of social

capital, although the parents lack high levels of financial and human resources.

Not only do Vietnamese in the community use informal networks to acquire infor-

mation, the information about their children and their children’s friends operates

on both levels discussed above. First, they garner immediate information about

youth, but secondly, the combined networking (application of social capital) has

an emergent effect of collective monitoring and sanctioning. These informal mech-

anisms encourage noncognitive behaviors in the children of the community, which

fosters academic achievement. The dense associations also became institutional-

ized into Vietnamese language and academic institutions, which translated directly

into cognitive skills.

Each form of capital is a dimension of a more fundamental concept: resources.

While the amount of resources available to a family for the purposes of their chil-

dren’s development seems quite simple on the surface, the preceding discussion

highlights how diverse the resources employed in raising the young are. While fi-

nancial and human capital are relatively “clean” concepts, the issues get “dirtier”

when we consider cultural and social capital because they reside partially within

and partially outside individuals in group contexts born of their social relationships

and the relationships of those around them. The problems are further compounded

when thought of as strictly individualized concepts, rather than as part of a larger,

more integrated schema, because there is certainly a significant degree of endo-

geneity in the system since one form of capital can facilitate gains in another.

However, despite ambiguities (and occasional contradictions) in these concepts,

employing them is useful because together they unify the concept of resources,

42

specify the mediating pathways of socioeconomic status (and to some extent de-

fine it), and embed the rational choice paradigm (perhaps imperfectly) into a social

framework. Furthermore, doing so allows individual-social structure interactions,

posits within-individual developmental consequences, and is consistent with both

continuity and change across generations.

The purpose here has been to review social capital, for completeness, as a type

of resource available to children through their parents in the context of resources.

Social capital, however, will not be treated substantively in the analysis phase,

although it must be noted that the underlying strategies of concerted cultivation—

participation in formal activities and parental involvement with other professionals

is likely help to create social capital for children. Lareau (2003) also notes, how-

ever, that families of different social classes have differing types of social capital,

with non-concerted cultivation families having more access to local, informal and

familial networks. In terms of academic achievement, however, one would clearly

suspect that the social capital developed and accessed by higher social class fami-

lies is more importantly related to children’s success (e.g. Horvat, Weininger, and

Lareau 2003).

2.5 The School & Classroom

The role that the family environment plays in the perpetuation of racial and so-

cioeconomic differentials falls into a larger discussion that includes school contexts.

Entwisle and Alexander (1993) note, along with Lareau (1988, 2003), that differ-

ences between the home and school environments for children who are not from

the middle class can be dramatic. Social group differences in cultural capital have

meaningful influences not only on the tangible skills that students bring with them

to school, but also on how those skills are shaped.

Lower SES children are much more often identified by their kinder-garten teachers as being at risk for serious academic or adjustmentproblems; they are absent more in first grade; and they receive lowerteacher ratings on behaviors related to school adjustment such as inter-est/participation and attention span/restlessness [the latter two stronglypredict later academic progress; see Alexander, Entwisle, and Dauber1993] (Entwisle and Alexander 1993: 407).

43

In addition,

The conventions of the school, with its achievement orientation, itsexpectations that children will stay on task and work independentlywithout close monitoring, its tight schedule of moving from lesson tolesson, its use of ’network’ English, its insistence on punctuality, andits evaluation of children in terms of what they can do instead of whothey are all can be daunting (Entwisle and Alexander 1993: 405).

Parental characteristics such as income and education probably play an impor-

tant role in determining the family’s residential location, which in turn influences

school access (Roscigno 1998). Somewhat independently of socioeconomic status,

racial bias in housing and lending markets importantly determines residential loca-

tions allocated to different social groups, with whites garnering the more favorable

locations (Massey and Denton 1993; Yinger 1995). Research suggests that segre-

gation between non-Hispanic whites and black, Hispanic, and Asian students, is

increasing, while segregation between black, Hispanic, and Asian students is de-

creasing in metropolitan areas (Reardon, Yun and Eitle 2000). Moreover, there is

substantial social group variation between schools and less variation within schools

in the earlier than later grades (Entwisle and Alexander 1993). This homogeneity

arises because elementary schools have smaller catchment areas that match resi-

dential neighborhoods (Entwisle and Alexander 1989). For these reasons, both the

social climate and organizational characteristics of schools may vary significantly,

imparting benefits for some groups relative to others.

Entwisle and Alexander’s (1992; Entwisle, Alexander, and Oldson 1997; En-

twisle and Alexander 1994; Alexander et al. 2001) research points to the impor-

tance of school composition, which was more important for reading achievement

than for mathematics. Even the poorest black children did better on math tests in

integrated than segregated schools, but scores diverged significantly for whites and

blacks in same-race segregated schools. One major finding was that the students in

integrated schools progressed more slowly over the school year than expected, par-

ticularly the black students. Although black children in racially segregated same-

race schools did better in winters, they tended to lose ground over the summers. In

fact, the black students in integrated schools learned more over the two-year study

only because of their much larger summer gains. Net of parental education and

44

gender, the white children’s reading scores did not differ significantly in winter or

summer by whether or not they attended a white-segregated or integrated school.

The purpose here is not to conduct a full-blown review of school and/or school-

ing effects, but to remind readers that compositional details are an important

consideration in studies of children’s academic achievement. These sources of vari-

ation will not be treated substantively in the analysis phase, however. Rather,

methods adjusting for relatively constant school-level factors will be used to elim-

inate confounding between parental and school characteristics.

2.6 The Summertime

At the nexus of discussions regarding family and school influences on achievement

are studies considering summer vacation influences on children’s test scores. These

studies are important because they break the year into two components: when

school is in session and when school is not in session. Separating school and family

effects can be difficult because parental characteristics and school characteristics

can be correlated in important ways (Roscigno 1998; Entwisle and Alexander 1989,

1993). In the summers, however, when children are not in school, families are the

most proximal source of influence on children’s academic growth. Thus, summer

slopes have been thought to provide the most accurate estimates of family influ-

ences on children’s achievement. Unfortunately, research on summer learning has

received little attention in the literature because of the data requirements (tests

must be administered in both the fall and spring for two years to provide estimates

of the school year and summer slopes).

Following students over time at multiple points in a year makes it possible

to begin disaggregating school from family effects. Although most large data sets

that have repeated measures are spaced too broadly to capture the seasonal nature

of learning, at the time of their publication, a review by Cooper, Nye, Charlton,

Lindsay, and Greathouse (1996) found that 39 studies had been conducted over the

course of the 20th century. Their meta-analysis of the 13 most recent and method-

ologically sound studies indicated that the summer break was more detrimental

for math than reading and that lower-class students’ reading skills decreases in the

summer while middle-class students’ skills remained the same or increased slightly.

45

The estimated magnitude of the summer differential was .1 standard deviation, an

amount that does not sound impressively large until one considers the temporal

nature of the learning process and realizes that summers over time can contribute

significantly to social group differences in achievement unless schools are able to

make up these losses.

The research by Alexander, Entwisle, and Olson (2001), Entwisle and Alexan-

der (1992, 1994) and Entwisle, Alexander, and Olson (1997) is notable because

their studies of Baltimore students begin in the first grade and captures summers.

On the whole, Alexander et al. (2001: 183) report that disadvantaged children

“keep up during the school year, but before they start first grade and in summers

between grades the out-of-school resources available to them are not sufficient to

support their achievement.” Although they found that black students had lower

math scores than white students, they were not the result of differences in either

summer or school-year learning (Alexander et al. 2001), unlike the findings of

Heynes (1978) who studied older children.

The patterns were somewhat different for reading than for mathematics (En-

twisle and Alexander 1994; Alexander et al. 2001). Although they found no racial

differences in reading scores once summer learning was accounted for, during the

school year both white and black children whose parents’ education levels were low

gained as much or more than their more advantaged peers, but when school was

open they fell behind. In some cases, the scores of children whose parents had low

education levels actually decreased over the summer.

More recent studies using the nationally representative ECLS-K have also at-

tempted to shed light on summer learning by looking at student gains between

kindergarten and first grade. In one study, black children, for example, were found

to learn fewer math-based skills over the summer, although there were no race

differences for reading (Lee et al. 2004). Higher social class children learned more

and lower social class children less, per expectations, for both math and reading

over the summertime. The evidence in their study that cultural capital, opera-

tionalized as a series of activities, is related to children’s summer learning is sparse

and not meaningfully related to race differences or class differences in children’s

reading, although class differences in math were somewhat ameliorated. Using a

more complex modeling strategy and different covariate list, however, Downey and

46

colleagues (2004; see also Reardon 2003 [who did not include Asians in the analy-

sis]), although using the same data, did not replicate Lee et al.’s results. Downey et

al. (2004) found that children gained few reading skills over the summer, although

Asian and higher social class children grew at significantly higher rates. All chil-

dren were found to learn math skills over the summer, with Asians again accruing

a significant advantage. There were no social class differences in children’s math

growth rates over the summertime.

The extent to which cultural capital, specifically concerted cultivation, differ-

entiates children’s summertime learning rates or is implicated in race and class

differences remains an open question—with both theoretical and policy implica-

tions.

2.7 Summary & Research Questions

Lareau’s (2003) concerted cultivation paradigm represents an important elabo-

ration which unifies two (sometimes disparate) conceptions of cultural capital.

Clearly resolving these conceptual distinctions13 is important because cultural cap-

ital is an idea important for understanding social class globally, in addition to

playing a key role in understanding the role of class and resources in the intergen-

erational transmission of advantage. On the one hand, researchers often consider

cultural capital principally a series of signals which are mostly arbitrary in nature,

while others have broader conceptualizations including aspects of the home envi-

ronment. Lareau’s (2003) work on parenting strategies illustrates the interplay of

these two perspectives, unifying them by elaborating the underlying cultural logic

with which parents act.

The practice of concerted cultivation is identifiable through a number of child-

oriented activities and parental behaviors. In seeking quickened child development,

parents who identify with its cultural logic expend great effort to structure their

children’s time through formal, organized activities. In doing so, these parents

seek to acquire experiences for their children that they hope will have lasting con-

sequences on their development through the accumulation of skills and beneficial

habits. Concerted cultivation practicing parents also tend to be more active with

13Which, admittedly, not all researchers acknowledge exist or consider particularly important.

47

teachers and other professionals, rarely hesitating to intercede on their children’s

behalf. These parents actively search for new learning materials and speak differ-

ently to their children, engaging them in conversations and encouraging them to

rationalize and defend their own actions, sometimes to the later irritation of parents

who discover that they are raising little ‘lawyers,’ and their children are not always

as obedient as they would prefer. Concerted cultivation is a very child-oriented

approach to raising children. While parents who follow the forms associated with

the accomplishment of natural growth no doubt care equally for their children,

they allow their children to develop in a more natural, relaxed fashion, without

the continuous drive towards the development of skills.

Lareau’s (2003) work raises a number of questions which will be addressed in

the analytic portion of the dissertation:

1. Do the parental behaviors and child activities identified by Lareau (2003),such as child activities, parental involvement with the school, and learningmaterials, covary systematically in a way consistent with the concept of ‘con-certed cultivation’?

2. Is ‘concerted cultivation’ stable over childhood? Given that concerted cul-tivation represents an underlying, often implicit strategy, the expectation isthat families should rank consistently on this measure over time.

3. How does concerted cultivation vary across population subgroups? Lareau’s(2003) observations suggest that (1) the association of concerted cultiva-tion with social class should be large, and (2) social class should be a moreimportant predictor than race. How this parenting strategy varies acrosspopulation subgroups is a question of considerable interest.

The experiences and nature of linguistic interaction between children and adults

in concerted cultivation oriented families implies real consequences for children’s

cognitive development (Lareau 2003: 244). In response, a second set of questions

relating concerted cultivation to general knowledge, mathematics, and reading

achievement are posed:

1. How is concerted cultivation related to children’s kindergarten readiness?Prior research suggests that children from families practicing concerted cul-tivation should have accrued significant advantages in school readiness bykindergarten entry.

48

2. Is concerted cultivation related to children’s growth over the summertime?Since schools are out of session, home environments are expected to be relatedto children’s growth over this period.

3. Does concerted cultivation have an impact during the school year after ad-justing for stable school and parental characteristics? If so, how strong isthis association, and does it account for race and social class differences ingrowth rates?

The following chapters turn to these questions in detail. Does large scale-

survey data representative of the United States population find support for or

against Lareau’s (2003) insightful and telling work? These results are important

not only for testing important ethnographic research, but also because understand-

ing the genesis of social group disparities in academic achievement is important in

a world of limited resources where familial disadvantage is all too often reproduced

and perpetuated across generations despite the best efforts of parents and child

professionals.

CHAPTER

THREE

Data & Methods

3.1 Data

The data used1 for this study are drawn from the Early Childhood Longitudinal

Study, Kindergarten Class of 1998-99 (ECLS-K). Developed under the sponsorship

of the U.S. Department of Education, National Center for Education Statistics

(NCES), the ECLS-K is a longitudinal study focusing on children’s early school

experiences beginning at kindergarten entry.

In general, the ECLS-K focuses on children’s 1) transition to school, 2)schooling and performance in the early grades, and 3) the interactionof school, family, and community... The four key issues addressed bythe ECLS-K are 1) school readiness; 2) children’s transitions to kinder-garten, first grade, and beyond; 3) the relationship between children’skindergarten experience and their elementary school performance; and4) children’s growth in math, reading, and general knowledge (i.e., sci-ence and social studies) and progress through elementary school (pg.2).

The holistic approach to children’s developmental issues across contexts and over

time makes the ECLS-K the most appropriate data set currently available to study

the relationship between parenting strategies and children’s academic achievement.

1The information for this section is from the ECLS-K Project Summary,http://nces.ed.gov/ecls/pdf/ksum.pdf.

50

3.1.1 The Design

The sampling frame2 for the ECLS-K is rather complex, being of a multistage

cluster design. First, primary sampling units (PSU) were drawn from 1990 county-

level population data and updated with information from the 1994 population

estimates of 5 year-olds by race-ethnicity from the U.S. Census Bureau. The

updated information on race was used to construct a measure of size to facilitate

the oversampling of Asian and Pacific Islander children (henceforth Asian). In the

next stage, a sample of 100 PSUs was drawn and then stratified based on size,

race, and 1988 per capita income.

Third, a sample of public and private schools offering kindergarten was drawn

from the sample of PSUs using the 1995-96 Common Core of Data3 (CCD) and the

1995-96 Private School Universe Survey4 (PSS). In 1998 the school sampled was

freshened so that newly opened schools or schools with newly available kindergarten

programs that were not in the CCD and PSS could be included. In addition,

Bureau of Indian Affairs (BIA) and Department of Defense (DOD) were directly

consulted since schools run by these organizations are generally not included in

the CCD. Within each PSU, schools were selected with probability proportional to

the size of the kindergarten cohort with public and private (divided into religious

vs. nonsectarian) schools constituting distinct sampling strata. “Within each of

these strata, schools were sorted to ensure good sample representation across other

characteristics” (pg. 4-5). After freshening the sample and ensuring eligibility of

schools, a total of 1,277 schools were drawn, 914 public and 363 private.

In the final stage, a sample of children was drawn from within schools using a

complete list of all kindergartners, including those with special needs.5 Within each

school, children were stratified into ‘Asian’ and ‘all other’ children with the goal

of 24 students per school.6 “Once the sampled children were identified, parent

2Information from this section is taken from NCES (2000) unless otherwise noted.3U.S. Department of Education, National Center for Education Statistics, Common Core of

Data, Public School Universe Survey, 1995-96.4U.S. Department of Education, National Center for Education Statistics. Private School

Universe Survey, 1995-96, NCES 98-229, by Stephen P. Broughman and Lenore A. Colaciello.Washington, DC: 1998.

5Note that classroom clustering was not built into the sampling frame. The ECLS-K consti-tutes a sample of schools, not classrooms.

6For the school sampling, in same cases small schools were grouped together.

51

contact information was obtained from the school” (pg. 4-8). Approximately

21,000 children were drawn into the sample, although for various reasons the sample

with available child data was 19,173, and the sample with some parent data was

18,097.

The first round of assessments took place in the fall of kindergarten. Children

were assessed in the spring of kindergarten, a 20% random subsample was assessed

in the fall of first grade, and the full sample was assessed again at the spring

of first grade and the spring of third grade.7 The sample was freshened in the

spring of kindergarten to include schools that were not operational during the fall

of 1998, and again in the spring of first grade to include children who did not

attend kindergarten.8 In addition, a 50% subsample of schools was selected where

children who moved were followed. By the end of third grade there are a total of

15,305 students, with 12,070 in public schools and 3,235 in private. In the spring

of third grade, 13,489 children had available parent data.

3.1.2 Dependent Variables

The ECLS-K9 uses a series of sophisticated cognitive batteries drawn from such

sources as the National Assessment of Educational Progress (NAEP) fourth-grade

test specifications of 1992, 1994, and 1996 to assess children general knowledge

(science and social studies), mathematics, and reading skills.10 As a tool to inform

public schooling policy, the ECLS-K cognitive batteries were designed so that

“tests should represent the typical and important cognitive goals of elementary

schools’ curricula” (pg. 2-1). The tests were individually administered because

young children are not experienced test takers, and the designers believed that

“individual administration could provide more sensitivity to each child’s needs

than a group-administered test” (pg. 2-2). In addition, the tests were not timed

so that children could take as much time as necessary.

The tests were also ‘adaptive’ in nature, which allowed each test to be suitable

to the child’s competence level. In adaptive testing, children are first given a

7Data for this section are drawn from NCES (2004).8There was no student freshening in the third grade sample.9Unless otherwise noted, information for this section are drawn from NCES (2002).

10In addition, there are also psychomotor and social rating scales.

52

routing test which is used to inform the test administrator of the child’s skill level.

“A child who is essentially performing on grade level should receive items that span

the curriculum for his or her grade. Children whose achievement is above or below

grade level should be given tasks with difficulty levels that match their individual

level of development at the time of testing, rather than a grade-level standard”

(pg. 2-3). The use of adaptive tests, by adjusting to the child’s skill level, reduce

both ceiling and floor effects so that the children’s skills could be assessed more

accurately.

The ability of adaptive testing procedures to adjust to children’s competency

levels is also important for assessing children over time.

Although the forms are tailored for individuals within a grade, theoverall grade-level forms should reflect core curriculum elements forthat particular grade. At the same time there must be overlappingitems in forms within a grade, as well as across grades. These linkingitems tie the vertical scale together both across forms within a gradeand across grades. About 20 to 30 percent of the items should overlapbetween adjacent grades (pg. 2-2). Adaptive testing relies on ItemResponse Theory (IRT) assumptions in order to place children whohave taken different test forms on the same vertical score scale (pg.2-3).

The ability to calibrate tests over time using IRT metric is key to appropriately

modeling achievement growth, particularly given the length of the study which, in

the sample used, is from kindergarten entry to the end of third grade.11

The process by which children’s achievement becomes translated into a mean-

ingful numeric scale then follows a number of stages. First, children are adminis-

tered routing tests so that appropriate items are used. Second, children are given

test elements suitable for the child’s level. Third, the test items are calibrated us-

ing a three-parameter IRT model which estimates the probability of a child getting

an answer given the child’s underlying ability. “The three-parameter IRT logistic

model uses the pattern of right, wrong, and omitted responses to the items admin-

istered in a test form and the difficulty, discriminating ability, and ‘guess-ability’

of each item, to place each test taker at a particular point, θ (theta), on a con-

11The ECLS-K will follow children through high school, although it is not clear whether ornot NCES will attempt to calibrate the tests only to the end of the fifth grade, which was theoriginal intention, or all the way through high school.

53

tinuous ability scale” (pg. 4-1). In these models, the item difficulty is essentially

the model intercept, the discrimination parameter is proportionally a slope, and

the ‘guess-ability’ a lower asymptote. Intuitively, these models are essentially non-

linear confirmatory factor analysis models, representing a generalization similar to

the extension from OLS to logistic (or probit) regression.

The average intercorrelations between the tests across waves are approximately

.75 for reading and mathematics, .57 for reading and general knowledge, and .66 for

mathematics and general knowledge. These routinely high correlations indicated

that children doing well in one area are expected to be doing well in the other

areas, particularly for math and reading.

General Knowledge Achievement

The general knowledge12 achievement test is evenly divided between the natural

sciences and social studies. This test was only administered through first grade;

later tests drop the social studies portion, so the following analyses assess gen-

eral knowledge from kindergarten entry the spring of first grade. For the science

portion of the battery, subject matter is evenly divided between earth and space

science, physical science, and life science. Particularly over the early grades, earth

and space science is principally concerned with questions about the relationship be-

tween the earth and other bodies in space, e.g., patterns of night and day, and the

seasons. Physical science includes matter and energy and their transformations,

and motion. Life science questions pertain globally to nature, more specifically to

interdependence, adaptation, ecology, and health and the human body.

The social studies segment of the general knowledge test is stratified into two

areas, denoted ‘knowledge’ and ‘analysis and interpretation.’ Although the social

studies portion of the test includes areas on history, government, culture, geog-

raphy, and economics, the tests are heavily weighted largely to culture (50 items

at kindergarten entry) and geography (20 items at kindergarten entry). The cul-

ture questions “includes a number of questions about everyday objects and their

uses (What do trains and planes have in common?) and social roles (What does a

fireman do?)” (pg. 2-15) while “[g]eography in the early grades typically includes

learning about where one lives in relation to the rest of the nation and the world,

12Unless otherwise noted, information for this section is drawn from NCES (2002).

54

gaining familiarity with maps and the globe, and learning about different types of

land and water and how people, plants, and animals have adapted to them” (pg.

2-15).

The test reliabilities range between .88 and .89 over the study period. Fur-

thermore, differential item functioning (DIF) indicated that few items or groups

of items behaved differently by race, class, or gender. In fact, differential item

functioning was more favorable across groups for the general knowledge test than

for either the mathematics or reading tests, despite the large mean differences in

test scores across social groups (Chapter 5).

Unlike the mathematics and reading tests, similar general knowledge tests have

not typically been administered in large surveys (e.g. the Children of the National

Longitudinal Study of Youth), meaning that this series of questions does not link

as easily to previous studies. The importance of this outcome for the present study,

however, derives from the broad applicability of the test itself. General knowledge

tests are noteworthy, particularly for researchers concerned with how families influ-

ence a broad range of children’s skills. Lareau (2003), for example, cites numerous

examples where parents engaging in a pattern of concerted development seek to

increase children’s knowledge of the world at every opportunity through the use of

questioning and refining definitions.

General knowledge tests are also relevant because often the items used to con-

struct these tests relate to a number of issues crossing through politics, history,

economics, and science, that may be more likely to manifest in conversations than

in purely reading or mathematics skills. Thus, general knowledge tests probably

reflect issues that translate across a number of domains through which children can

quickly signal their competencies and that, furthermore, are strongly implicated in

the diverse array of experiences children in concerted cultivation oriented families

are exposed to.

Mathematics Achievement

The mathematics13 battery assesses children across a number of content strands.

Areas include (pgs. 2-7 & 2-8):

13Unless otherwise noted, information for this section are drawn from NCES (2002).

55

• Number Sense, Properties, and Operations. This refers to children’s un-derstanding of numbers (whole numbers, fractions, decimals, and integers),operations, and estimation, and their application to real-world situations.Children are expected to demonstrate an understanding of numerical rela-tionships as expressed in ratios, proportions, and percentages. This strandalso includes understanding properties of numbers and operations, ability togeneralize from numerical patterns, and verifying results.

• Measurement. Measurement skills include choosing a measurement unit,comparing the unit to the measurement object, and reporting the resultsof a measurement task. It includes items assessing children’s understandingof concepts of time, money, temperature, length, perimeter, area, mass, andweight.

• Geometry and Spatial Sense. Skills included in this content area extend fromsimple identification of geometric shapes to transformations and combina-tions of those shapes. The emphasis of the ECLSK is on informal construc-tions rather than the traditional formal proofs that are usually taught inlater grades.

• Data Analysis, Statistics, and Probability. This includes the skills of col-lecting, organizing, reading, and representing data. Children are asked todescribe patterns in the data, or making inferences or drawing conclusionsbased on the data. Probability refers to making judgments about the like-lihood of something occurring based on information collected on past oc-currences of the event in question. Students answer questions about chancesituations, such as the likelihood of selecting a marble of a particular colorin a blind draw when the numbers of marbles of different colors are known.

• Patterns, Algebra, and Functions. Consistent with the NCTM [Commissionon Standards for School Mathematics of the National Council of Teachersof Mathematics (NCTM 1989).] kindergarten to fourth-grade curriculumstandards, the ECLSK framework groups pattern recognition together withalgebra and functions. Patterns refer to the ability to recognize, create,explain, generalize, and extend patterns and sequences. In the kindergartentest, the items included in this category consist entirely of pattern recognitionitems. As one moves up to the subsequent grades, algebra and functionitems are added. Algebra refers to the techniques of identifying solutionsto equations with one or more missing pieces or variables. This includesrepresenting quantities and simple relationships among variables in graphicalterms. It should be noted that while pattern recognition is relatively heavilyemphasized in kindergarten and even first-grade classrooms, the proposedframework tends to de-emphasize the assessment allocation since it is notclear what to expect with reference to longitudinal trends in this skill area.

56

The largest category of time spent on items across waves are drawn from number

sense, properties, and operations. The radiabilities across the first four waves of

data ranged from .92 to .94. Differential item function indicated few differences

across groups, with the possible exception being a Hispanic-white difference at the

spring of first grade (the report was released before third grade data were available).

As one of the critical areas of knowledge specified in NCLBH, understanding the

genesis of early child-skill differentials is of key importance so that schools can

use their resources most effectively. Children’s mathematics achievement from

kindergarten entry through the third grade is the focus of Chapter 6.

Reading Achievement

As with general knowledge and mathematics, the reading14 battery assessed chil-

dren across numerous content strands, as follows (pg. 2-10):

• Initial understanding requires readers to provide an initial impression orglobal understanding of what they have read. Identifying the main pointof a passage and identifying the specific points that were drawn on by thereader to construct the main point that would be included in this category.

• Developing interpretation requires readers to extend their initial impressionsto develop a more complete understanding of what was read. It involves thelinking of information across parts of the text, as well as focusing on specificinformation.

• Personal reflection and response requires readers to connect knowledge fromthe text with their own personal background knowledge. Personal back-ground knowledge in this sense includes both reflective self-understanding,as well as the broad range of knowledge about people, events, and objectsthat children bring to the task of interpreting texts.

• Demonstrating a critical stance requires the reader to stand apart from thetext and consider it objectively. This is includes questions asking about theadequacy of evidence used to make a point, or the consistency of someone’sreasoning in taking a particular value stance. In kindergarten and first grade,some questions about unrealistic stories were asked to assess the child’s no-tion of real vs. imaginary. Such story types allow us to get information oncritical skills as early as kindergarten.

14Unless otherwise noted, information for this section is drawn from NCES 2002.

57

Over the first four waves, the reliability estimates ranged from .93 to .97. There

were a few cases of differential item functioning (DIF) between white and black,

Hispanic, and Asian children. As explained in the psychometric report,

[i]n all cases where the DIF occurred against the minority group, theitems were submitted to the fairness committee for review and all theitems were passed by the committee. In other words, a judgment wasmade that the difference in performance was due to skill differences con-sistent with the test specifications and not due to factors that wouldunfairly bias the item against the subgroup. It should be kept in mindthat there are 72 reading items in the test forms for kindergarten and92 for first grade. With five sets of comparison groups and four roundsof data, more than 1,500 comparisons are made in an attempt to sta-tistically identify items showing DIF, so chance alone could account forsome of the findings. In an achievement test covering reading develop-ment where children are growing very fast but at quite different rates,one might identify DIF at one time point and then see it reduced or goaway later in the children’s development (pg. 5-10).

The reading assessments are further complicated because of language issues

for Hispanic children. The ECLS-K administered the Oral Language Development

Scale (OLDS) to assess children who had a non-English language background. Only

children who passed the OLDs were administered the reading test, so the sample

at each given wave is only representative of those children who showed a basic level

of English competency.15 Children’s reading achievement from kindergarten entry

through the third grade is the focus of Chapter 7.

3.1.3 The Sample & Control Variables

Race & Social Class

Descriptive statistics for the sample are presented in table 3.1. The approximate

sample size at kindergarten entry with available data is 14,152 cases–although it

should be noted that this wave-specific sample size fluctuates due to the sample

refreshing, in addition to family mobility and attrition. The sample is approxi-

mately 60% white, 13% black, 17% Hispanic, and 5% Asian, with a heterogenous

‘other’ category comprising the remaining 5%.

15For mathematics, children were given an alternative Spanish translation.

58

Tab

le3.

1.E

CLS-

KW

eigh

ted

Sam

ple

Mea

ns,St

anda

rdD

evia

tion

s,an

dP

ropo

rtio

nsfo

rth

eTot

alSa

mpl

e,by

Rac

ean

dSo

cial

Cla

ss,fo

rth

eC

ontr

olV

aria

bles

atK

inde

rgar

ten

Ent

ry(A

ppro

xim

ate

N=

14,1

52)

Rac

eSo

cial

Cla

ss

Tot

alW

hite

Bla

ckH

ispa

nic

Asi

anB

.25

%M

.50

%T

.25

%

Sam

ple

Size

(N)

1415

283

6618

3324

1877

432

6272

2136

69%

ofTot

al59

.12

12.9

517

.09

5.47

23.0

551

.02

25.9

3W

hite

12.3

454

.24

33.4

2B

lack

39.7

749

.37

10.8

6H

ispa

nic

47.3

542

.56

10.0

9A

sian

20.5

443

.41

36.0

5So

cial

Cla

ss0.

050.

30-0

.42

-0.5

00.

28-1

.11

-0.0

81.

33(s

d)(0

.99)

(0.9

1)(0

.92)

(0.8

8)(1

.09)

(0.5

8)(0

.37)

(0.5

5)A

geat

K.E

ntry

(Mon

ths)

65.5

665

.99

65.1

364

.80

64.6

565

.26

65.6

465

.66

(sd)

(4.2

4)(4

.24)

(4.0

8)(4

.23)

(4.0

3)(4

.23)

(4.2

5)(4

.24)

Fem

ale

0.49

0.49

0.50

0.50

0.51

0.50

0.49

0.50

Seco

ndK

.0.

040.

040.

050.

050.

040.

060.

040.

03N

on-E

nglis

hLan

g.at

Hom

e0.

130.

010.

010.

490.

600.

300.

080.

06St

epPar

ent

0.08

0.08

0.07

0.07

0.02

0.10

0.09

0.03

Sing

lePar

ent

0.20

0.12

0.54

0.21

0.08

0.36

0.18

0.09

Oth

erFa

mily

Stru

ctur

e0.

020.

010.

030.

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59

Social class is a standardized measure composed of parental occupation, educa-

tion, and income. The scale is computed as the average of the z-scores across these

items for each child, with each item being used to compose the measure given equal

weight.16 White and Asian children are both similarly advantaged regarding social

class (about .3 standard deviations), while both black and Hispanic children are

similarly disadvantaged (over .4 standard deviations). Interestingly, Asian families

are the most variable in terms of social class and Hispanic families the least.

The final three columns of table 3.1 decompose social class into the bottom

and top quartiles, and the middle 50% of the distribution. Children in the lower

quartile come from homes, on average, over 1 standard deviation below the mean

on social class, while children from homes in the upper quartile are over 1.3 stan-

dard deviations above the mean. The distribution of race/ethnic groups across

social class strata highlight the relative advantage of white and Asian children,

particularly given their increased probability of being in the upper quartile of the

distribution, compared to the black and Hispanic children who are over-represented

in the lower quartile.

Child Variables

In addition, a number of control variables are included in the table and used

in the later regression analyses. The child’s age in months at kindergarten entry

represents an important characteristic to adjust for because all children are learning

at rapid rates during this time of their lives and older children have, obviously, had

more time to learn and mature (Reardon 2003; Downey et al. 2004; Burkam et al.

2004). Because the beginning age of kindergarten entry is relatively standardized,

there are few differences by race or social class. The average child begins school

at approximately 66 months or 5.5 years of age. This variable is centered at the

grand mean (66 months) in subsequent analyses.

Additional covariates include whether or not the child is female and whether

16Supplementary work suggests that despite collapsing a significant amount of data into ascalar entity, adding the individual items, while contributing more detail, does not really providesignificantly more information. In addition, including the items individually adds complexityto the analysis because the researcher is then posed with difficult problems such as, “Whatdo I do about father’s education for children without fathers?” as well as having interpreta-tional/parceling questions relating to variables that are highly intercorrelated.

60

or not the child is a second time kindergartner. The probability that the child is a

second time kindergartner does not vary strongly by race/ethnic status, although

second timers are more likely to be lower than higher socioeconomic status.

Family Variables

Because this analysis includes heterogenous groupings such as ‘Hispanic’ and ‘Asian,’

who are likely to be from immigrant families, it is important to adjust for the fact

that children from these families are likely to experience a non-English language

environment in the home. In this case, whether or not a non-English language

is spoken in the home is constructed to differentiate these children, who, in the

home-based context, may not be able to get help with English-based reading skills.

Asian, Hispanic, and low socioeconomic children are far mare likely to come from

these homes than white, black, or middle and higher class children.

Family structure has been shown to be importantly related to children’s aca-

demic success and is also importantly related to both race and social class. Two-

parent, continuously married families represent the baseline, comparison group,

with additional categorizations comprising step parent family, either mother or fa-

ther,17 single parent, generally single mother, and an other category comprised of

children living with their grandparents and other miscellaneous groupings. Well

over one-half of white children live with both biological parents, although nearly

20% of white children live with a single parent at kindergarten entry. Over one-half

of black children live with a single parent and about 40% live with married parents.

Proportions for Hispanic children are similar to those for whites, while the vast

majority of Asian children live with both biological parents. In terms of family

structure, Asian and upper quartile social class families are distributed similarly.

Not surprisingly, as social class lowers, the proportion of children in single parent

family increases.

The mother’s age may be important since older mothers are likely to have

more stable employment and higher education, and, in general, to have more life

experience and greater maturity. The mother’s age at kindergarten entry is lower

for more disadvantaged groups. This variable is centered at the sample grand-mean

(33.2 years) in subsequent analyses.

17The vast majority of these families have a stepfather.

61

Mother’s employment status is also included, with full-time employment the

baseline category. The indicator categories are mother works parttime and mother

does not work. Across all race/ethnic categorizations, mothers are most likely to

work part time. Black mothers have the lowest unemployed rate, and a significantly

higher part-time work status rate than mothers of other groups (60% vs about 43

– 49%). The proportion of mothers working full-time and part-time increases

with increasing social class, although the lowest level of maternal unemployment

is found for the middle class.

In addition, whether or not the mother worked prior to the child’s birth is

included. White and black mothers are both more likely to have worked than

either Hispanic or Asian mothers, and lower social class mothers are less likely

than either middle or upper class mothers. In all cases, over 60% of mothers

worked, however.

Additional Covariates

Parental educational aspirations for their children, which is an obvious proxy for

parental academic orientations, have been found to be related to children’s later

academic achievement. To get more than a high school education but less than

a graduate degree is the reference category with indicators for high school degree

or less or a graduate degree or higher expectation is included in the analysis.

Black parents are the most likely to think that their children will not go far in

school (12%), but are also more likely than white parents to think that their

children will get a graduate degree. Over 40% of both Hispanic and Asian parents

believe their children will get graduate degrees, proportions that are higher than

the expectations for high social class parents. Over 20% of lower class parents

believe that their children will not go beyond high school.

Children’s early experiences differ not only with respect to their home environ-

ments but also with regard to pre-schooling experiences. No care is the reference

category, with home-based care, head start, and center-based care coded as dummy

variables. Across social groups, the proportion receiving home-based care is rela-

tively static. Not surprisingly, however, disadvantaged groups are more likely to

have received head start, while advantaged groups are more likely to have received

some form of center-based care.

62

3.2 Methods

The analysis proceeds in two conceptually distinct sections. The first analysis

addresses the ability to identify ‘concerted cultivation’ and study the relationship

between this concept and the covariates discussed in the previous section. The

second phase of the analysis relates concerted cultivation to growth in the three

academic outcomes, general knowledge, mathematics, and reading achievement.

3.2.1 Concerted Cultivation

Since ‘concerted cultivation’ is a latent construct, variables that Lareau (2003)

describes as being related to this construct must be identified, then allowed to

covary under a suitable parametric specification (discussed in more depth next

chapter). The approach taken is one of ‘confirmatory factor analysis’ (CFA), which

is a type of factor analysis where the underlying model is specified and checked

against the observed covariance structure of the items. The details will be discussed

in more extensively in the following chapter; however, at this stage it is important

to note that the indicators used are not normally distributed, which raises potential

difficulties in the analysis. In response to this, an approach analogous to the IRT

models (van der Linden and Hambleton 1997; De Boeck and Wilson 2004) discussed

above are used to conduct a nonlinear CFA. In this case, a two-parameter IRT

model with a probit link function is used. The general two-parameter IRT model,

which estimates the probability, Pi(θ) that an individual with ability θ “gets an

item correct,” δi is formulated as,

Pi(δi = 1|θ) =1

1 + exp−ai(θ−bi), (3.1)

where ai is the item discrimination, bi is the item difficulty. However, in practice

there is no special reason to use the logit model. In this case, a probit model is

used,

Pi(δi = 1|θ) = Φ(αiθ + βi), (3.2)

63

where Φ(·) is the standard normal cumulative distribution function. The general

notation, expressed as a probit function is

Pi(δi = 1|θ) = Φ(ai(θ + bi)), (3.3)

which implies that αi = ai, and

bi =βi

αi

. (3.4)

In the next chapter, the model parameters will be expressed as α and β from

model 3.2, where α is the ‘discrimination’ parameter which is the slope or fac-

tor loading, and β is the item ‘difficulty’ or intercept. Both the CFAs specifying

the hypothesized concerted cultivation model and subsequent regression models

predicting concerted cultivation use a limited-information, weighted least square

estimator18 with robust (sandwich) standard errors and mean- and variance- ad-

justed chi-square statistics, allowing for missing data on observed outcomes (i.e.,

the indicators used to define the CFA) in Mplus19 v3 (Munthen and Munthen

2004).20 All analyses use the weights provided by the ECLS-K so that results are

generalizable to the U.S. population of kindergartners in 1998.

Although Mplus allows for an impressive range of sophisticated models to be

fit to the data, it is not quite flexible enough to accommodate the growth model

used for the subsequent analyses. The problem, essentially, is that although Mplus

allows for individually varying times of observation, piecewise growth models with

individually varying times of observation cannot be fit to the data.21 By fixing

the factor loadings on the growth parameters as is typical in latent growth curve

modeling, it is possible to estimate piecewise growth models. For the problem at

hand, however, this approach has certain flaws.

The advantage of conducting all analyses in Mplus is that the structural equa-

tion modeling (SEM) framework allows the incorporation of the CFA directly, ame-

liorating measurement in the latent, concerted cultivation construct (e.g. Bollen

18Using the ‘WLSMV’ estimator.19A small grant by the Penn State Research and Graduate Studies Office (RGSO) Dissertation

Support System allowed me to purchase this software and manual.20Using the ‘type = complex missing’ commands21In the structural equation modeling framework, individually varying times of observation

represent a significant integration problem.

64

1989). So, in this case, a decision had to be made between (1) adjusting for mea-

surement error in concerted cultivation but fixing the growth parameters to the

average observation times, and (2) leveraging the varying times of observations to

more accurately represent the underlying data generating process using HLM v6

software, but treating concerted cultivation as a ‘known’ variable which is mea-

sured without error. In this case, option (2) appears preferable because bias in the

associations between concerted cultivation and academic achievement are likely to

be downwards (supplementary analyses suggest this is the case), so that coefficient

estimates represent a lower bound. For case (1), if time in school is related to

children’s growth, which it is, then not accounting for variability in times of ob-

servation will misappropriate variance in children’s test scores. By not accurately

capturing the data generating process, it is not clear how coefficient estimates will

be biased. Although this approach is less than optimal, it is an appropriate for-

mulation given the practicalities of the situation. In order to do this, factor scores,

which are not free of measurement error (Bollen 1989), are output from Mplus and

then input to HLM as a known variable.22

3.2.2 Academic Achievement

After the distribution of concerted cultivation across population subgroups has

been analyzed, the next step is to incorporate the concerted cultivation factor

scores into a model of children’s growth. The basic structure of the model is drawn

from the familiar random-effects model (Raudenbush and Bryk 2002; Singer and

Willett 2003) following Reardon (2003; see also Downey et al. 2004; March and

Cormier 2002). The level 1, within-student model, where t indexes within-unit

observations, i indexes students, and j indexes schools, is represented as:

Ytij = π0ij + π1ijKtij + π2ijStij + π3ijFtij + π4ijTtij + εtij,

22For information on how Mplus computes factor scores from the posterior distribution, seethe online technical appendices: http://www.statmodel.com/mplus/techappen.pdf

65

and the level 2, between-student model where the πpij are rates representing points

gained per month:

πpij = βp0j +

Q∑q=1

βpqjXq{t}ij + η(2)p.ij, (3.5)

and the level-3, between-school model:

βp0j = γp00 + η(3)p..j, (3.6)

βpqj = γpq0, (3.7)

where K is time in kindergarten, S is summer duration, F is time in first grade,

and T is the the length of time from the first grade to the third grade assessment.23

The above formulation says that growth is a piecewise function of time; kinder-

garten, a summer break, first grade, and the second and third grades. Initial

status and the temporal slopes are allowed to vary between students and schools

randomly, conditioned on between-student variables Xq{t}ij. Generally, level-2 co-

variates are not allowed to vary across occasions, but in this case the between-child

covariates are included as time changing covariates specific to the period. This is

possible because the piecewise approach breaks the timeline into meaningful seg-

ments. The η(l) represent random effects or deviations at level l and are assumed

MV N(0,Ψ(l)) within level and orthogonal across levels. In addition, because there

are up to five observations per child and five child-level random effects, the level-1

variance is fixed using precision weights.24

The model is presented graphically in figure 3.1, where it can be seen that

children are allowed to grow at distinctive rates over different periods of their early

schooling careers. Coefficient estimates in these models, however, can be biased if

the between-student variables Xq{t}ij are correlated with the random effects, Ψ(3),

or, in other words, if there is a significant “between” school relationship between

the outcome and group-means. In response to this potential source of bias (see

23Details on the creation of the timing variables are available in Reardon (2003).24In older versions of HLM, the weights were in fact precision weights, 1

(1−α)σ2 , where α isan estimate of the test reliability and σ2 is the variance of the tests. HLM v6, however, nowdoes the inversion so the weights are constructed as (1−α)σ2, which is the estimate of the true,wave-specific variance.

66

Figure 3.1. Graphical Depiction of Children’s Academic Growth Model

15

25

35

45

55

65

75

85

95

Ach

ieve

men

t

Kindergarten Summer 1st Grade 2nd & 3rd Grade

Raudenbush and Bryk 2002, chapter 5) a second set of school level “fixed-effects”

models is estimated. In the HLM context, this is accomplished by group-mean

centering the between-child covariates,

πpij = βp0j +

Q∑q=1

βpqj

(Xq{t}ij −Xq{t}.j

)+ η

(2)p.ij, (3.8)

which is analogous to controlling for the group means, Xq{t}.j, at the school-level,

but is ultimately more parsimonious and does not produce multicollinearity. Fur-

thermore, there are a very large number of parameters estimated in these models

already. Note that because the characteristics of children’s schools change, as do

student-specific values, the decreasing nesting structure of the data due to movers

is accommodated by allowing the school means to be period-specific. To not allow

for a temporal adjustment of school means potentially undermines the basic as-

sumption of the fixed-effects model of constant aggregate influences. In this case,

the parameter estimates are period-specific fixed-effects.25

25Furthermore, to the extent that school-level fixed-effects models are required to get unbiasedestimates of ‘average within-school’ relationships, group-mean centering is easily accomplished inthe known-variable case, but becomes significantly difficult in latent variable approach. Although

67

Because the growth model estimates period-specific parameters, a covariate is

included for whether or not a child moved over a given period (Reardon 2003,

dropped children that moved). In the presence of movers, the nesting structure

of the data becomes non-constant, but is too sparse to estimate a cross-classified

model. This means, in essence, that there is a degree of model misspecification

because the computation of the random effects assumes constant nesting. In prac-

tice, this probably means that some of the between-child variance is absorbed into

the higher level, between schools. Because, in this case, focal interest is on the

regression parameters and less on the variance components, it is more important

to salvage degrees of freedom, particularly when one considers the large number

of parameters and the reduced sample in the fall of first grade (which is needed to

estimate the summer and first grade slopes).26

Although mixed-models can handle missingness on the dependent variable with

some flexibility, other techniques need to be used when right-hand-side covariates

are missing. Five multiple imputation data sets are generated (Allison 2001; Little

and Rubin 1987) for the independent variables allowing for categorical variables.27

Imputation was done as a flat file with wave-specific variables distributed as the

columns (see Allison 2001), including the dependent variables. Imputed values

for the dependent variable, however, are not used (see Downey et al. 2004: 619).

Sample sizes increase from a maximum of 11,000 cases to 16,202. The sample sizes

vary with outcome,28 however, so the exact numbers are reported in the outcome-

specific analytic chapters.

it is possible to do so by creating a between-unit CFA and fixing the loadings, the number ofintegration points necessary exceeded the memory capacity of the computers I had available.

26Dropping movers does not substantially change the results.27Using mvis.ado for Stata v8 which allows variables to be distributed normal, ordinal, logit

or probit, and multinomial. See Royston (2004), the software developer28NCES re-scales the tests at each wave when new data arrive. Unfortunately, it has been

their policy to re-scale tests only for those children who took the test. This places unfortunatelimitations on the sample sizes for the math and reading tests because of the smaller wave 5sample size. In principal, there is no reason why the tests should not be recalibrated for allchildren who have been assessed at least once.

CHAPTER

FOUR


4.1 Introduction

Of central concern to a study linking parenting strategies to children’s academic

achievement is the question of whether the available survey data can be used to

identify those strategies. The purpose of this chapter is to (1) identify such a con-

struct, and then (2) look at the children’s differing exposure levels. Lareau’s (2003)

research, while rich in detail, is faced with the classic problem of ethnographic re-

search: the ability to generalize from the small-scale sample and contexts within

which the observations are embedded. Survey research has the opposite prob-

lem: Under the mechanics of random sampling, generalizations can be offered, yet

there is a distinct problem of detail and process. Indeed, detail in survey research

is fundamentally parametric, particularly for more sophisticated questions, while

ethnographic is by nature non-parametric. The strengths of these two perspectives

should be taken as complementary, particularly when the non-parametric obser-

vations of ethnographic research can formulated and recast parametrically with

generalizable survey data. A combination of these two perspectives can add fur-

ther validation (or the converse) to ethnographic research, while providing deeper

theoretical meaning and insight into the parametric survey component.

69

4.2 Confirmatory Factor Analysis

4.2.1 The Covariate List

Concerted cultivation is not a single variable, there is no simple question such as

“do you practice ‘concerted cultivation’” that survey researchers can ask. Nor is

it even a unidimensional construct, being composed of a single set of inter-related

behaviors or actions. Rather, concerted cultivation is a multidimensional idea for

which Lareau (2003) has noted three principal dimensions, as expressed in table

2.1. The underlying parenting strategy or cultural logic that Lareau (2003) recog-

nized are identified by pronounced differences in (1) the organization of children’s

daily lives, (2) language use, and (3) parental interventions with institutions. Par-

ents who practiced concerted cultivation used formal activities with professionals

and adults to structure their children’s time and to provide access to a host of

experiences which provide children with training for school and later labor mar-

ket success. These parents were also involved with their children’s teachers and

schools, constantly monitoring these environments to ensure they matched with the

educational aspirations they held for their children. Language use also tended to

be geared more towards reasoning and the elicitation of responses. Without either

detailed observational studies of the family linguistic environment (see Hart and

Risley 1995 for a unique quantitative treatment) or a highly correlated proxy, such

as parental cognitive scores (perhaps), language use is a problematic dimension.

The bulk of Lareau’s (2003) discussion dealing with the organization of chil-

dren’s lives focused on extracurricular activities as a means of garnering both

educational and social experiences for their children. The first panel of table

4.1 lists variables in the ECLS-K that measure children’s participation in extra-

curricular activities—which, it must be remembered, children participate in en-

tirely at parental discretion. Activities include dance classes, organized athletics,

clubs, music lessons, art classes or lessons, and a more general participation in

organized activities category.

Direct indicators of parental institutional interventions are somewhat difficult,

although the ECLS-K provides a number of items presumably related to such a

concept as it relates to schools. In panel 2 of table 4.1, a number of items related

to parental participation with various aspects of the children’s schools are listed.

70

Table 4.1. Measures Used to Specify the Concerted Cultivation CFA

Variable Description

Outside of school hours, has CHILD ever participated in:

P2DANCE Dance lessons?

P2ATHLET Organized athletic activities, like basketball, soccer, baseball, or gymnastics?

P2CLUB Organized clubs or recreational programs, like scouts?

P2MUSIC Music lessons, for example, piano, instrumental music or singing lessons?

P2ARTCRF Art classes or lessons, for example, painting, drawing, sculpturing?

P2ORGANZ Organized performing arts programs, such as children’s choirs, dance programs,

or theater performances?

Since the beginning of this school year, have you or the other adults in your household

P2ATTENB Attended an open house or a back-to-school night?

P2ATTENP Attended a meeting of a PTA, PTO, or Parent-Teacher Student Organization?

P2PARGRP Gone to a regularly-scheduled parent-teacher conference with CHILD’s teacher or

meeting with CHILD’s teacher?

P2ATTENS Attended a school or class event, such as a play, sports event, or science fair?

P2VOLUNT Acted as a volunteer at the school or served on a committee?

P2FUNDRS Participated in fundraising for (CHILD)’s school?

Material Resources

P1CHLBOO About how many children’s books does CHILD have/are in your home now,

including library books? Please only include books that are for children. (ln)

Lareau (2003, 1988) and other authors have documented widely different parental

participation rates in their children’s schools by social class in ways highly consis-

tent with the concerted cultivation paradigm. Parent-teacher meetings represent a

significant opportunity for parents to interact with teachers, and most higher class

parents practicing concerted cultivation capitalize on these opportunities, using

them to interview and assess their children’s teachers. Other opportunities such as

open houses and back-to-school nights represent important opportunities to net-

work and gather information on teachers, as do various meetings, school and class

events, volunteering and serving on committees, and fundraising.

There are no direct measures of the parental linguistic patterns, nor is there

direct cognitive information such as IQ tests for parents. Although this represents

a potential gap in the ability of this study to fully capture ‘concerted cultivation,’

it is possible to identify other material resources in the home, whose correlation

with language use is probably quite strong.1 Although the number of books in

the home is a less than ideal proxy for language use, academic materials may

1Maternal ability (AFQT) has been show to operate at least partially through cognitive stim-ulation in the environment (Guo and Harris 2000).

71

constitute a significant dimension of their own.2 Although numerous questions

pertaining to the presence of newspapers and other reading materials in the home

were asked, these variables are not available across waves. The number of books

that are specifically the child’s is the only measure of material academic resources

available across waves.3

4.2.2 The Model

The concerted cultivation CFA model is depicted in figure 4.3. According to this

model, an underlying latent factor which, after Lareau (2003), I have referred

to as ‘concerted cultivation,’ produces the latent traits or dimensions discussed

above, namely the underlying propensity for parents to participate or intervene

with school, the underlying propensity to structure children’s lives through activ-

ities, and the underlying propensity for parents to seek out and acquire learning

materials. These latent traits then lead parents to behave and act differently, which

are identified by the observed measures. The key idea here is that it is not so much

the number of child activities or ways that parents interact with school, but the

underlying propensity or disposition, which, in fact, results from the underlying

parenting strategy that guides parental behaviors and actions.

How measures of language use would influence or relate to the underlying model

is an open question. However, to the extent that the model implied by Lareau

(2003) and shown in figure 4.3 is consistent with the data, the underlying ‘con-

certed cultivation’ measure likely captures the elements of language use which are

highly correlated with the other factors. To the extent that these items exhibit the

expected ‘covariance structure,’ elements of language use are probably captured in

the model, albeit imperfectly.

Residual correlation in the model between dance lessons, music lessons, and

participation in the general organized activities categories are necessary because

2Lareau (2003) notes, for example, the differing availability of learning materials in concertedcultivation practicing families, and, in particular, the extent to which parents seek to cultivatechildren’s interests by seeking out materials.

3In actuality, the natural logarithm of the number of books is taken since, firstly, it is hardto imagine that an additional book means the same for a child who has 100 as compared to 5books. Second, large variances in structural equation modeling can lead to estimation problems,which is in fact the case here. Taking the ln decreases the variance to a level more constant withthe other covariates in the model.

72

Figure 4.1. Graphical Depiction of the Concerted Cultivation CFA

Co

nce

rted

Cu

ltiv

atio

n

P2

AT

TE

NB

P2

AT

TE

NP

P2

PA

RG

RP

P2

AT

TE

NS

P2

VO

LU

NT

P2

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RS

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DA

NC

EP

2A

TH

LE

TP

2C

LU

BP

2M

US

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1

73

of the non-uniqueness of the last category. In addition, allowing correlations be-

tween these items improves model fit. Furthermore, the latent factor associated

with material academic resources is fixed to the observed child books indicator to

identify this portion of the model.

4.2.3 1 Wave CFA

The CFA results for concerted cultivation are presented in table 4.2. The coef-

ficients for the ‘discrimination’ or slope parameters and ‘difficulty’ or intercepts

are presented in the probit metric (see chapter 3). These parameter descriptions

pertain to child activities and parental participation which are modeled in the IRT

formulation; the other loadings are interpreted in the usual fashion. Furthermore,

the difficulty parameters are partially standardized in relation to the underlying

latent construct, which means they represent the change in the probability of a ‘1’

(as opposed to ‘0’) on the item for a standard deviation change in the underlying

factor, i.e. child activities.

To facilitate interpretation, figure 4.2 depicts the item characteristic curves

plotting the predicted probability of the actions and behaviors for the standardized

underlying factor (for a review of the model parameters see section 3.2.1). The

item characteristic curve for child activities highlights that athletics are the most

common form of activities, and that for higher θ the probability that children will

participate in the various activities is extremely high, while the probability is quite

low for children from families with a low propensity. The story for the other items

is somewhat similar, although the probability of participation does not approach

1, the children who are more likely to participate are clearly well to the right of

the mean in the distribution.

Parent participation is most clearly captured towards the middle of the distri-

bution for parental participation. Probabilities at the mean for almost all forms of

participation are relatively high (approximately .5) while the probabilities generally

approach 1 for high propensity parents and 0 for less inclined parents, although par-

ents across the distribution attend parent-teacher meetings. These results indicate

that parents to the right of the mean are highly likely to participate with children’s

schools in various ways, while those parents on the left side of the distribution are

74

Table 4.2. Results for the Kindergarten Concerted Cultivation CFA. The ‘Discrimination’ Pa-rameters are Analogous to Factor Loadings Standardized with Reference to the Latent Variable,and the ‘Difficulty’ Parameters are Intercepts

Discrimination Difficulty

Child ActivitiesDance Lessons 0.547 -0.977Athletics 0.679 -0.121Clubs 0.508 -1.114Music 0.509 -1.445Arts and Crafts 0.456 -1.473Organized Performing Arts 0.446 -1.050

Parental ParticipationAttend Open House, Back to School Night 0.683 0.631Attend PTA, PTO, etc., Meeting 0.392 -0.404Attend Parent-Teacher Meeting 0.395 1.004Attend School or Class Event 0.618 0.409Volunteer at School or Serve on Committee 0.710 -0.055Participated in School Fundraiser 0.552 0.233

Material Resourcesln(# of Books +1) 1.000

Concerted Cultivation (Standardized Loadings)Child Activities 0.850Parental Participation 0.908Material Resources 0.602

Summary StatisticsR2 Child Activities 0.722R2 Parental Participation 0.824R2 Material Resources 0.362

χ2 (df = 43)a 497.024RMSEA 0.025CFI 0.949TLI 0.958

N 16,402

75

rarely involved, and that when they are, it is usually to attend parent-teacher

meetings.

The middle panels of table 4.2 displays the fully-standardized factor loadings for

the continuous parent participation, child activities, and material resource factors

on concerted cultivation. The child activities loading is .85, while that for parent

participation is even larger, .91. The correspondence suggests that the activities

and parent participation latent variables or propensities can in large measure be

exchanged or interchanged with the ‘concerted cultivation’ factor. As implied

by the high loadings, the latent-variable R2 values are high, .72 and .82 for child

activities and parent participation, respectively. The loading for material resources

is also respectively high, being over .6 (implying an R2 of .36).

In addition to the high degree of interrelationships amongst the measures com-

posing concerted cultivation measure, which is consistent with the expectations

derived from Lareau’s (2003) work, the model fit statistics suggest good overall

model fit. The RMSEA of 0.025 is well within norms of ‘good model fit’, as are

the CFI (.949) and TLI (.958). The χ2 fit is highly statistically significant, but

this is not surprising given the large sample size (N = 16, 402). Taken together,

the overall suggestion is that the observed variables are related in a fashion highly

consistent with the patterns observed by Lareau (2003).

4.2.4 3 Wave CFA

The ECLS-K repeats the measures used to define concerted cultivation in the

previous section again at the end of the first and third grades. The results for

a model depicting a simultaneous estimation of all three CFAs are are presented

in table 4.3 and a graphical representation of the between wave correlations are

depicted pictorally in figure 4.3. The ranking of the children’s families on the

latent concerted cultivation factor over the 4-year span is remarkably consistent.

Correlations over .95 between waves and a correlation of nearly .94 over a 4-year

span, suggests remarkable consistency in the concerted cultivation measure over

time. These results mean that to the extent that parental behaviors change, those

behaviors and actions change proportionally for every family in the population.

The overall suggestion is one of remarkable consistency, which we would expect

76

Figure 4.2. Item Characteristic Curves for Child Activities and Parent Participation

0

.2

.4

.6

.8

1

Pro

babi

lity

−4 −3 −2 −1 0 1 2 3 4Theta

Dance Lessons

Athletics

Clubs

Music

Arts/Crafts

Org. Arts

Item Characteristic Curve for Child Activities

0

.2

.4

.6

.8

1

Pro

babi

lity

−4 −3 −2 −1 0 1 2 3 4Theta

Open House

Meeting

Parent−Teacher

School Event

Colunteer

Fundraiser

Item Characteristic Curve for Parent Participation

77

Figure 4.3. Between-Wave Correlations for ‘Concerted Cultivation.’

Concerted

Cultivation:Kindergarten

Concerted

Cultivation:1st Grade

Concerted

Cultivation:3rd Grade

ρ=0.985 ρ=0.969

ρ=0.939

given the theoretical nature of concerted cultivation—that it is a parenting strategy

that reflects an underlying cultural logic or form of cultural capital.

A question of interest in this regard is the degree of factorial invariance in the

loading characteristics across waves. Unfortunately, the large N (16,925) means

that this sample is overpowered and able to detect trivial between-wave differences

in the parameter estimates when using χ2 goodness-of-fit difference testing.4 Vi-

sual inspection of the parameter estimates suggest a high degree of correspondence

across waves, although a few indicators deviate—usually decreasing in importance

over time. The story, overall, however, is highly consistent and impressive when

one considers the myriad changes that this period of childhood encompasses. Fur-

thermore, the factor loadings regressing the first-order latent variables on concerted

cultivation5 are notably consistent over time, which means that although there is

a little bit of ‘movement’ relating the items to child activities and parent partic-

ipation, the relationship of concerted cultivation with these factors and material

resources is for all practical purposes invariant.

4Due to the estimator used for the parameter estimates, Mplus must use a special test toconduct difference tests. See the technical appendices online at www.statmodel.com for details.

5Which also means that the R2 values are highly consistent across waves.

78

Table 4.3. Concerted Cultivation CFA at Kindergarten, First Grade, and Third Grade. TheItem Discrimination Parameters are Standardized with Respect to the Latent Variables

Kindergarten First Grade 3rd Grade

Item Discrimination (Factor Loadings, Slopes)

Child Activities

Dance Lessons 0.569 0.492 0.251

Athletics 0.685 0.689 0.555

Clubs 0.509 0.571 0.539

Music 0.485 0.510 0.459

Arts and Crafts 0.442 0.460 0.307

Organized Performing Arts 0.428 0.417 0.362

Parental Participation

Attend Open House, Back to School Night 0.679 0.692 0.680

Attend PTA, PTO, etc., Meeting 0.373 0.373 0.348

Attend Parent-Teacher Meeting 0.379 0.416 0.360

Attend School or Class Event 0.627 0.673 0.699

Volunteer at School or Serve on Committee 0.718 0.784 0.817

Participated in School Fundraiser 0.555 0.578 0.614

Material Resources

ln(# of Books +1) 1.000 1.000 1.000

Item Difficulties (Intercepts)

Child Activities

Dance Lessons -0.977 -0.881 -1.137

Athletics -0.120 0.140 0.236

Clubs -1.114 -0.569 -0.459

Music -1.445 -1.260 -0.889

Arts and Crafts -1.473 -1.241 -1.198

Organized Performing Arts -1.050 -0.865 -0.722

Parental Participation

Attend Open House, Back to School Night 0.631 0.772 0.969

Attend PTA, PTO, etc., Meeting -0.404 -0.210 -0.144

Attend Parent-Teacher Meeting 1.004 1.173 1.315

Attend School or Class Event 0.409 0.609 0.813

Volunteer at School or Serve on Committee -0.055 -0.032 -0.026

Participated in School Fundraiser 0.233 0.401 0.456

Concerted Cultivation (Standardized Loadings)

Child Activities 0.877 0.876 0.908

Parental Participation 0.896 0.876 0.825

Material Resources 0.591 0.571 0.548

Across Wave Correlations

Kindergarten 1.000

First Grade 0.979 1.000

3rd Grade 0.936 0.970 1.000

Summary Statistics

R2 Child Activities 0.769 0.768 0.824

R2 Parental Participation 0.802 0.767 0.680

R2 Material Resources 0.349 0.326 0.301

Fit Indices, Full Model RMSEA CFI TLI

N = 16, 925 0.025 0.947 0.967

χ2 = 1, 763∗∗∗, df = 150a

79

4.2.5 Summary & Notes

These results provide further evidence supporting the concerted cultivation para-

digm. Not only is the general factor observable in the data consistent with Lareau’s

(2003) observations, but the system of relationships defining this factor over time

also show a high degree of consistency over a 4-year span, which is a long period

when dealing with young children. In addition, given the theoretical orientation

towards concerted cultivation as a model of action expressing cultural capital or

a cultural logic, the results completely conform to expectations of temporal con-

sistency. Due to the high consistency of concerted cultivation over time, both

in family rankings within the distribution and the factor structure across waves,

subsequent analyses will use the kindergarten scores.

Before continuing, there are two notes or addendums that must be made. First,

the CFA model reported is one of many that was tested. Researchers familiar

with the ECLS-K have probably noted the absence of the ‘cognitive environment’

measures (HOME subscales) detailing questions pertaining to reading to the child,

playing games, etc. These items were consistent with concerted cultivation during

kindergarten, though the loadings dropped significantly over time. This is mostly

the result of the age-graded nature of those often-used items. In addition, due

to the highly consistent rankings of families on the concerted cultivation measure,

including the additional items has only a trivial impact on the subsequent analyses.

Second, it would be disingenuous to continue the discussion without noting

that I have conceptualized ‘concerted cultivation’ in a way that is not entirely con-

sistent with Lareau (2003). The underlying distribution of concerted cultivation

presented has assumed a continuous, mostly normal-shaped distributional form.

Lareau (2003: 30 & 236) explicitly rejects what she describes as gradation, ex-

plicitly positing categorical analysis. Statistically, this implies latent class analysis

(see Clogg 1995) rather than the more traditional CFA approach undertaken here,

leaving the question of which approach fits the data better for future research.

However, it should be noted that (1) Lareau (2003), in a small, localized sample

would have been unable to observe the full range of population heterogeneity (e.g.

Chin and Phillips 2004), and (2) the proposed model appears to fit the data quite

well. Rather than categorizing ‘concerted cultivation’ and ‘the accomplishment of

natural growth,’ this analysis has considered them to be poles on a continuum. I

80

return to this issue in more detail in the final chapter.

4.3 Predicting Concerted Cultivation

Results for a regression model series predicting concerted cultivation are presented

in table 4.4. All reported coefficients are standardized with respect to concerted

cultivation so that the coefficients for dummy variables reflect standardized differ-

ences, the coefficient for social class is fully-standardized, while the coefficients for

the non-dichotomous remaining covariates refer to the variables in their natural

metric. The first column presents ‘bivariate’ models while columns A–D display

results for a series of multiple regression models with increasingly complex speci-

fications.

Initial race differences are quite large, nearly over a standard deviation for all

groups except ‘others,’ although these differences are not as large as those for

social class. Notably, these race differences are far larger than those expected from

Lareau’s (2003), probably because of the truncated population heterogeneity in her

sample. Two children differing by two standard deviations on the social measure

are expected to come from families differing by over 1.4 standard deviations of

concerted cultivation. Social class explains nearly 50% of the variance in concerted

cultivation, while race explains over 25%. Together these two variables, race and

social class, explain nearly 60% of the variance, which suggests that most of the

variance attributable to race is in fact due to differences in social class across

race/ethnic groups.

The coefficients associated with both race and class decrease when both are

entered in the same model (A). The social class parameter shrinks by about 110

th

of a standard deviation while the magnitude of the black and Hispanic coefficients

decrease by nearly .4 standard deviations. The gap widens for Asians, however,

suggesting that Asian children of the same social class as white children come from

families over 1 standard deviation lower on concerted cultivation. The coefficients

decrease in magnitude when the family covariates are added to the equation (model

C), although all the race and social class differences remain large. Both the Asian

and Hispanic parameters decrease largely in model C when the non-English lan-

guage spoken in the home is added. This result is intuitive; and since concerted

81

cultivation represents a form middle-class U.S. cultural capital, it is not surprising

that immigrant parents are likely not familiar with these practices and so do not

manifest them. Children from families where a non-English language is spoken in

the home come from families that, on average, score nearly .82 standard deviations

lower on the concerted cultivation measure.

Although children from single parents come from lower concerted cultivation

homes relative to two-parent, biological families, the largest family structure dif-

ference is from step parent homes, where parents appear to be significantly less

engaged. In addition, the mother’s work status is related to concerted cultivation

with children whose mothers work part time or not at all, scoring significantly

lower on concerted cultivation, even after adjusting race, social class, and other

family covariates. Parents with low expectations for their children are also far less

likely to engage in concerted cultivation. The initial difference is huge, over -.9

standard deviations, and remains sizeable even after the full covariate list is added

to the model (-.4 standard deviations).

The covariate list explains a large proportion of the variance in concerted culti-

vation. As noted above, the single largest predictor is social class (.48%), with race

contributing significant additional explanatory power (.59%, an additional 10%).

When the full-covariate list is added nearly 70% of the total variance in concerted

cultivation is explained.

4.4 Concerted Cultivation: The Distribution

In the following chapters, concerted cultivation is used to predict child academic

achievement from kindergarten entry over the early grades. As noted in Chapter

3, software limitations preclude the possibility of estimating the piecewise growth

model with varying times of measurement while incorporating the CFA directly

into the equation, so factor scores are output and treated as ‘known’ in HLM v6.6

One way to check the consistency of the factor score with the latent variable is

6I have also done some work attempting to estimate the full model using WinBUGS, a Bayesiananalysis program. Although this appears to be possible, computation time is staggeringly long,necessitating the compromise approach adopted. For an introduction to Bayesian statistics,which is where ‘multilevel modeling’ draws its inspiration, see Congdon (2003), Gelman, Carlin,Stern, and Rubin (2003).

82

Table 4.4. SEM Partially-Standardized Regression Coefficients for Models Predicting Con-certed Cultivation (N = 14, 152)

Model

Variables Bivariate A B C D

Black −1.018 ** −0.666 ** −0.657 ** −0.573 ** −0.587 **(0.055) (0.039) (0.039) (0.035) (0.034)

Hispanic −1.160 ** −0.729 ** −0.719 ** −0.357 ** −0.370 **(0.045) (0.034) (0.033) (0.033) (0.033)

Asian −0.930 ** −1.013 ** −1.004 ** −0.578 ** −0.573 **(0.063) (0.046) (0.047) (0.049) (0.048)

Other −0.605 ** −0.374 ** −0.359 ** −0.275 ** −0.259 **(0.061) (0.045) (0.043) (0.041) (0.039)

Social Class 0.707 ** 0.595 ** 0.596 ** 0.495 ** 0.426 **(0.015) (0.013) (0.013) (0.013) (0.013)

Age at K. Entry 0.021 ** 0.008 ** 0.007 ** 0.007 **(0.004) (0.002) (0.002) (0.002)

Female 0.161 ** 0.155 ** 0.160 ** 0.148 **(0.022) (0.018) (0.017) (0.016)

Second K. −0.359 ** −0.091 −0.091 −0.049(0.070) (0.054) (0.049) (0.048)

Non-English Lang. at Home −1.319 ** −0.815 ** −0.853 **(0.049) (0.038) (0.038)

Step Parent −0.591 ** −0.298 ** −0.284 **(0.044) (0.033) (0.033)

Single Parent −0.781 ** −0.180 ** −0.192 **(0.042) (0.026) (0.026)

Other Family Structure −0.002 −0.094 −0.073(0.091) (0.069) (0.067)

Child/Adult Ratio - 1 −0.272 ** −0.081 ** −0.059 **(0.019) (0.013) (0.012)

Mother’s Age (centered) 0.057 ** 0.012 ** 0.011 **(0.002) (0.002) (0.002)

Mother Works Part-Time −0.444 ** −0.234 ** −0.245 **(0.033) (0.023) (0.023)

Mother Doesn’t Work −0.446 ** −0.130 ** −0.111 **(0.036) (0.027) (0.026)

Mother Worked Prior to C. Birth 0.196 ** 0.006 −0.021(0.031) (0.021) (0.021)

High School or Less Ed. Exp. −0.930 ** −0.400 **(0.045) (0.031)

Graduate School Ed. Exp. −0.029 0.066 **(0.031) (0.020)

Home Based Care 0.320 ** 0.133 **(0.038) (0.028)

Head Start −0.402 ** −0.021(0.056) (0.036)

Center-Based Care 0.708 ** 0.247 **(0.038) (0.026)

R2 .27a,.48b 0.587 0.597 0.663 0.687

‘m’ p < .06, ‘*’ p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.a R2 for the race bivariate regression.b R2 for the social class bivariate regression.

83

Table 4.5. Concerted Cultivation Distributional Characteristics by Race and SocialClass

Variable X Sx Min Max

Total −0.010 1.004 −3.444 2.941Race

White 0.373 0.836 −3.444 2.941Black −0.559 0.918 −3.444 2.568Hispanic −0.598 1.000 −3.444 2.216Asian −0.511 1.015 −3.444 2.338

Social ClassBottom 25% −0.873 0.868 −3.444 2.139Middle 50% 0.084 0.842 −3.444 2.632Upper 25% 0.688 0.786 −2.485 2.941

to read the factor score back into the SEM package, correlating it with the factor

estimated as the CFA. I tried this but was not able to get the model to converge—

probably due to the high correlation, which suggests a high degree of consistency

between the latent and observed scores. In addition, factor scores computed us-

ing different models also generally correlate between .97 and .99. Although the

evidence suggests a strong degree of correspondence between the latent variable

and factor score, which implies that measurement error should play a small role in

biasing coefficient estimates, Mplus estimates produce larger effect sizes than the

‘observed’ analysis using HLM. However, it is not clear if these larger effect-sizes

are in fact artifacts caused by the inability to incorporate varying times of obser-

vation into the piecewise growth model. In practice, it is safe to assume that the

HLM results presented in the next chapters are slightly biased downwards, and the

Mplus results which are not shown are probably biased upwards as a result of the

underestimation of between-child variances.

Table 4.5 presents descriptive statistics for the concerted cultivation factor score

by race (figure 4) and social class (figure 4.5), and race and social class (figure 4).

Whites are the only group to encompass the full range of the concerted cultivation

distribution (table 4.5 and figure 4), and, although most groups span a large range

of the distribution, there are acute dissimilarities across groups, as we expect,

given the previous analysis. It should be noted, however, that the race differences

in means for the factor score are smaller than those in the SEM models in table 4.4.

84

This finding suggests race and concerted cultivation are less strongly associated

when using the factor scores as compared to the CFA because of measurement

error, implying that the ability of concerted cultivation to explain race gaps in

children’s academic achievement is lessened as a result when using factor scores.

Again, as stated previously, results such as these are consistent with the notion

that factor scores introduce a degree of downward bias in coefficient estimates.

Table 4.5 and figure 4.5 also demonstrate differences in the coverage of the

concerted cultivation distribution by social class. Although the standard deviations

are relatively consistent, there are striking dissimilarities in the means, as expected

from table 4.4. Histograms by race and class are presented in figure 4. Lower-class

white children, although likely to come from disadvantage concerted cultivation

families, are more likely to come from the positive side of the distribution than

children in other groups. The tendency toward negative concerted cultivation

values for lower social class minority group members will have implications for the

achievement analyses in chapters 5, 6, and 7.

4.5 Discussion

Lareau (2003) describes a number of differences in the ways that parents organize

their children’s lives, interact with professionals, speak, and utilize academic re-

sources. These practices identify the underlying parenting strategies, embodying a

cultural logic which is a form of parental cultural capital. Although Lareau’s (2003)

study is ethnographic, by noting patterns of behaviors and actions, the observa-

tions offered lend themselves to parametric model formulation using latent variable

modeling. Although ethnographic research is often limited in its generalizeability

without verification using techniques designed to capture broader ranges of popula-

tion heterogeneity, when confirmation is found, the ethnographic research provides

deeper insights into the data-generating process observed in the estimated relation-

ships than survey data can provide alone. In this case, data using the ECLS-K

find broad support for the notion of ‘cultural capital’ when expressed as a pattern

of relationships between variables. Not only are the patterns of relations amongst

the indicators consistent with concerted cultivation, the parameter estimates are

relatively invariant over the 3-year study period, and parents rank nearly perfectly,

85

Figure 4.4. Concerted Cultivation by Race

0

5

10

15

Per

cent

−4 −3 −2 −1 0 1 2 3 4

White

0

5

10

15

−4 −3 −2 −1 0 1 2 3 4

Black

0

5

10

15

Per

cent

−4 −3 −2 −1 0 1 2 3 4

Hispanic

0

5

10

15

−4 −3 −2 −1 0 1 2 3 4

Asian


implying remarkable consistency in parenting strategies over time.

One unfortunate limitation of the model is a notable inability to directly capture

parental language use. However, given the strong support found for the other

dimensions of the cultural capital model, it is likely that elements of language

use are captured by the latent constructs through correlations with the observed

outcomes. Obviously, however, one expects that the model would be stronger if

items measuring language use were included.

The models predicting concerted cultivation support Lareau’s (2003) findings

that social class is the defining characteristic differentiating families. Indeed, social

class explains nearly 50% of the variance in concerted cultivation. Race, however,

appears to play a larger role than Lareau (2003) suggests, which is not really

86

Figure 4.5. Concerted Cultivation by Social Class

0

5

10

15

Per

cent

−4 −3 −2 −1 0 1 2 3 4

Full Sample

0

5

10

15

Per

cent

−4 −3 −2 −1 0 1 2 3 4

SES: Middle 50%

0

5

10

15

Per

cent

−4 −3 −2 −1 0 1 2 3 4

SES: Bottom 25%

0

5

10

15

Per

cent

−4 −3 −2 −1 0 1 2 3 4

SES: Upper 25%


surprising when one considers the limited racial heterogeneity in social class in her

study.7 Although race/ethnic group indicators remain strong, important predictors

with increasingly complex model specifications, inclusion of the race categories

explains about another 10% of the variance above and beyond social class.

The strong negative effect of having a non-English language spoken in the home,

a clear proxy for immigrant status, also provides further evidence in support of

the ‘concerted cultivation’ interpretation of the CFA model. Because concerted

7There was also limited heterogeneity in racial composition; white and black children were theonly two groups included in Lareau’s (2003) analysis, and it is not likely that the full distributionwas covered for either of these groups. In addition, Lareau did not include either Hispanic orAsian families, two composite groups that are extremely heterogenous along a number of socialdimensions. Note that I too ignore some of the variability across these groups by using the broadcategorizations ‘Hispanic’ and ‘Asian.’

87

Figure 4.6. Concerted Cultivation by Social Class & Race

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=1294

Low SES: White

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=5284

Middle SES: White

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=3231

High SES: White

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=1021

Low SES: Black

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=1123

Middle SES: Black

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=274

High SES: Black

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=1422

Low SES: Hispanic

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=1217

Middle SES: Hispanic

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=311

High SES: Hispanic

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=263

Low SES: Asian

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=469

Middle SES: Asian

0

5

10

15

20

Per

cent

−4 −3 −2 −1 0 1 2 3 4N=370

High SES: Asian


88

cultivation is a U.S. family cultural repertoire, it would be surprising to find that

recently arrived immigrant families have adopted middle-class parenting practices.

Although many parents probably come to adopt the cultural logic of concerted

cultivation, they are likely to be higher social class parents, and, importantly,

time must be give for cultural diffusion. While including the non-English language

indicator explains significant portions of the differences in concerted cultivation

of Hispanic and Asian families relative to whites, the models indicate that Asian

families have lower levels of concerted cultivation than white families of the same

social class. Coefficients are reduced for both black and Hispanic families when

social class is included, as expected however.

Net of social class and race, single-parent families practice less concerted cul-

tivation than two-parent biological families. The disadvantage is even larger for

step parent families. These findings are consistent with previous research sug-

gesting that stepfathers are less involved with children than biological fathers.

Furthermore, these differences in family structure do not appear to be strictly

the result of temporal constraints since mothers who are employed full time are,

on average, more likely to practice concerted cultivation than part-time and non-

employed mothers. While parents who expect their children to get graduate or

professional degrees practice marginally more concerted cultivation, parents with

low educational expectations for their children practice considerably less. The

sizeable deficit highlights how parental goals or expectations for their children can

operate through parental actions.

Without the guiding hand of Lareau’s (2003) ethnographic research, it not

clear from the previous literature that the items used for the CFA analysis should

cohere in the fashion they do. The diverse number of ways the cultural capital

has been used in the literature suggests that researchers have been attempting to

operationalize cultural capital in a somewhat haphazard fashion. Given the lack

of clear guidance and cultural dissimilarities between the U.S. and France, where

the concept was developed by Bordieu, this is not surprising. However, Lareau’s

(2003) work outlines an American model which links across many dimensions of

child and family life, creating a rich backdrop for the development of hypotheses.

Furthermore, Lareau’s (2003) richly detailed study highlights the underlying logic

of the practice of concerted cultivation, giving the latent variables operational-

89

ized in the CFA deeper theoretical meaning than simply an interesting covariance

structure amongst observations, and illustrating the different ways that parents

view their roles. Having found confirmation for Lareau’s (2003) conception of cul-

tural capital as a parenting strategy denoted ‘concerted cultivation,’ the remaining

analytic chapters relate this concept to children’s academic growth.

CHAPTER

FIVE

General Knowledge Achievement

5.1 General Knowledge Achievement Growth

Research on childhood academic achievement has tended to focus on reading and

mathematics achievement. Part of this focus is simply the product of the availabil-

ity of these tests in large data sets, but also reflects the importance of these skills

in other areas of academic competence. General knowledge1 tests are notewor-

thy, however, particularly for researchers concerned with how families influence

a broad range of children’s skills. Lareau (2003), for example, cites numerous

examples where parents engaging in a pattern of concerted development seek to

increase children’s knowledge of the world at every opportunity through the use of

questioning and refining definitions.

General knowledge tests are relevant because the items used to construct these

tests relate to a number of issues extending across politics, history, economics, and

science, that may be more likely to become manifest in conversations and the gen-

eral content of parent-child interactions than purely reading or mathematics skills.

In addition, because the questions composing these tests cross numerous academic

domains, general knowledge tests probably reflect a knowledge-base through which

children can quickly signal their competencies to teachers and other professionals

and which will, later in life, play roles similar in the labor market and social life.

1It’s important to note that “general knowledge” is an achievement, not an IQ test.

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Denton et al. (2000) have demonstrated very large general knowledge skill

differences across social groups. Although previous research assessing test scores

across numerous domains using the NAEP data (Lapp, Grigg, and Tay-Lim 2002)

suggest group disparities across a number of academic domains (see also Hedges

and Nowell 1998; Phillips et al. 1998b; Jencks and Phillips 1998), those found in

the ECLS-K are larger than expected, which has tempered researchers’ interest in

the test (e.g. Fryer and Levitt 2004). It is worth reiterating at this point that the

characteristics of general knowledge test across social groups are actually better

than those for the other tests (NCES 2002).

5.2 General Knowledge Growth

Results for two descriptive, baseline growth models are presented in table 5.1 for

baseline comparisons with later models. The growth parameters are arrayed across

the columns, rather than down the rows as is typical. The first unconditional

model includes no control variables, while the second conditional baseline model

includes covariates for age at kindergarten entry, whether the child is a second

time kindergartner, and whether the child changed schools over a given period—

covariates which are included in all subsequent models. Children begin school with

a mean general knowledge achievement test score of approximately 20 points with

a standard deviation of (√

35.8 =) 6 points. Test scores increase at over .8 points

per month over the kindergarten year, nearly two times faster than during the

summertime, and rise somewhat slower during first grade (.7 points per month).

The variances on the slope parameters are small but significant. Small variances

indicate that, on average, children grow at similar rates over the study period.

Surprisingly, and in contrast to the results for mathematics and reading scores

(chapter’s 6 and 7), variability of the summertime between children is smaller

than for either kindergarten or first grade, although differences between-school are

larger. Children, on this measure, appear to grow at relatively uniform rates. The

between-student random effect correlations (level-2) indicate that children who

begin the test with higher scores grow at slower rates than children in the same

schools during the subsequent periods, with the negative correlation increasing with

time. Surprisingly, the summer and first grade growth rates are nearly collinear

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Table 5.1. Null Growth Models for General Knowledge Achievement Scores (IRT) fromKindergarten Entry Until Spring of Third Grade

Intercept Kindergarten Summer 1st

Null Model (w/out Covariates)Growth Parameters 20.004 0.822 0.425 0.663

(0.156) (0.008) (0.030) (0.010)

L-2 Random Effects1

Intercept 35.787Kindergarten −0.120 0.062Summer −0.299 0.135 0.0261st −0.507 0.254 0.949 0.039

L-3 Random Effects1

Intercept 20.296Kindergarten 0.097 0.024Summer −0.070 −0.316 0.1451st −0.263 −0.014 −0.527 0.024

Null Model (w/ Covariates)Growth Parameters 20.376 0.824 0.422 0.664

(0.151) (0.008) (0.030) (0.010)Second Time K. −1.085 −0.072 −0.169 −0.025

(0.300) (0.028) (0.108) (0.035)Age at K. Entry 0.371

(0.012)Changed Schools 0.047 0.087 −0.046

(0.037) (0.045) (0.035)

L-2 Random Effects1

Intercept 32.693Kindergarten −0.120 0.062Summer −0.299 0.135 0.0261st −0.507 0.254 0.949 0.039

L-3 Random Effects1

Intercept 18.570Kindergarten 0.071 0.024Summer −0.027 −0.324 0.1391st −0.292 −0.016 −0.523 0.024

Note: Columns represent level-1 parameters in the growth models.1 Diagonals are variances and off diagonals correlations.

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with a correlation of approximately .95. The between-level correlations follow a

similar, albeit attenuated pattern.

A very large proportion of the total variance in children’s general knowledge

test scores resides between schools. Schools with higher than average initial treat-

ment tended to have lower average growth rates during the first grade, although

the average level of initial status is not related to the other growth parameters.

Average growth rates over the summertime also tend to be lower when average

kindergarten growth was higher. The variances around the school-year growth pa-

rameters are both small and similar, while there is more variability in growth over

the summertime. The between-school variability in summertime growth is larger

than for any of the other variance components, including those at the between-

student level.

The results of the initial, baseline growth model highlight the following:

1. Although there is significant variability in children’s test scores at kinder-garten entry, there is also significant variability in the average levels of stu-dent achievement between schools.

2. Growth rates are relatively uniform during the school year, both betweenchildren and and between schools.

3. Children who begin kindergarten with more skills tend to learn at slowerrates than children in the same schools over subsequent periods.

4. Variability in summer growth rates between children is large, and further-more, summer and first-grade learning are highly collinear. The largestgrowth parameter variability occurs in the summer slope between schools.

5.2.1 Non-Centered Growth Models

Results for a series of three-level growth models predicting children’s general knowl-

edge growth over the kindergarten and first grade years are presented in table 5.2.

In addition, coefficient reduction summaries and standardized coefficients are avail-

able in Appendix tables A.1 and A.2. All coefficients for equations A through F are

reported in the table while the full model results for model G are available in Ap-

pendix table A.4 (along with a series of intermediate models).2 Models A through

2Non-focal parameters are not reported in the main table because the large number of para-meters results in excessive table length.

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C are descriptive in nature, estimating what is essentially the “bivariate” relation-

ship between race (A), social class (B), and concerted cultivation (C) on children’s

general knowledge trajectories. The next model set in the series, D and E, adjusts

the race (D) and social class equations (E) for difference in concerted cultivation

levels. Race, social class, and concerted cultivation are included simultaneously in

model F, while the full control list is entered into the equation in model G.3 All

models adjust for whether or not the child is a second time kindergartner, age at

kindergarten entry, and whether or not the child moved during a given period.

Initial Status

The first panel of regression coefficients in table 5.2 represent differences in chil-

dren’s expected general knowledge achievement scores at kindergarten entry. Dif-

ferences across social groups are striking. Hispanic, Black, and Asian children

(model A), for example, score between 4.4 and 5.3 points (.73 and .89 standard de-

viations4) lower than whites, on average. These race differences are far larger than

those reported on the other tests (see Chapters 6 and 7). Disparities by social class

(model B) are also large—nearly 2.5 points for each standard deviation increase

on the social class composite (.42 standard deviations on the test). The gap for

children two standard deviations apart is nearly .85 standard deviations, a tremen-

dously large difference in children’s general knowledge as assessed at kindergarten

entry.

Before estimating the extent to which these social group differences in children’s

general knowledge scores at kindergarten entry are due to differences in parenting

practices, it is important to uncover the simple relationship between concerted

cultivation and children’s scores. The disparity by the concerted cultivation mea-

sure is larger in magnitude (model C) than that for social class,5 2.9 points for

each standard deviation, which is a difference of .49 standard deviations on the

test. Children are clearly differentiated with regard to their early knowledge base

by the parenting strategies of their parents, as expected from the differences in

3Results and full covariate list for model G are displayed in Appendix table A.4.4The standardized race differences are computed by dividing the coefficient by the standard

deviation of the corresponding variance component from the first null model in table 5.1, whichin this case is the intercept.

5The statistical significance of this statement was not assessed.

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Table 5.2. Growth Models for General Knowledge Achievement Scores (IRT) from Kinder-garten Entry Until Spring of Third Grade by Race, Social Class, Concerted Cultivation, andSelected Covariates (ECLS-K)

Model

Variablesa,b A B C D E F G

Initial Status 22.238 ** 20.350 ** 20.358 ** 21.697 ** 20.345 ** 21.625 ** 21.416 **(0.134) (0.116) (0.108) (0.109) (0.095) (0.099) (0.193)

Black −4.669 ** −3.446 ** −3.191 ** −3.094 **(0.205) (0.196) (0.189) (0.193)

Hispanic −4.378 ** −3.137 ** −2.841 ** −1.731 **(0.189) (0.184) (0.179) (0.188)

Asian −5.292 ** −3.528 ** −4.039 ** −2.178 **(0.274) (0.267) (0.261) (0.281)

Other Race −2.926 ** −2.219 ** −2.157 ** −1.877 **(0.276) (0.263) (0.256) (0.251)

Social class 2.492 ** 1.692 ** 1.681 ** 1.328 **(0.065) (0.068) (0.068) (0.071)

Concerted Cultivation 2.926 ** 2.602 ** 2.359 ** 2.017 ** 1.635 **(0.065) (0.067) (0.069) (0.071) (0.073)

Second K. −0.895 ** −0.625 * −0.714 * −0.605 * −0.478 −0.395 −0.348(0.290) (0.294) (0.283) (0.277) (0.281) (0.275) (0.275)

Age at K. Entry 0.358 ** 0.384 ** 0.373 ** 0.362 ** 0.383 ** 0.371 ** 0.373 **(0.012) (0.011) (0.011) (0.011) (0.011) (0.011) (0.010)

Kindergarten Slope 0.835 ** 0.824 ** 0.825 ** 0.835 ** 0.825 ** 0.834 ** 0.856 **(0.009) (0.008) (0.008) (0.009) (0.008) (0.009) (0.014)

Black −0.040 * −0.042 * −0.039 * −0.043 *(0.018) (0.019) (0.019) (0.020)

Hispanic −0.026 −0.021 −0.019 −0.023(0.018) (0.018) (0.018) (0.019)

Asian −0.009 −0.004 −0.005 −0.018(0.026) (0.027) (0.027) (0.029)

Other Race 0.009 0.008 0.009 0.008(0.026) (0.026) (0.026) (0.026)

Social class 0.011 0.010 0.009 0.003(0.006) (0.007) (0.007) (0.007)

Concerted Cultivation 0.009 0.007 0.006 0.004 0.006(0.006) (0.007) (0.007) (0.007) (0.008)

Second K. −0.070 * −0.067 * −0.071 * −0.071 * −0.068 * −0.068 * −0.070 *(0.028) (0.028) (0.028) (0.028) (0.028) (0.028) (0.028)

Changed School 0.050 0.059 0.081 * 0.080 * 0.083 * 0.082 * 0.084 *(0.037) (0.037) (0.037) (0.036) (0.036) (0.036) (0.036)

Summer Slope 0.415 ** 0.423 ** 0.425 ** 0.411 ** 0.426 ** 0.412 ** 0.379 **(0.036) (0.030) (0.030) (0.037) (0.030) (0.037) (0.050)

Black 0.082 0.096 0.104 0.100(0.073) (0.075) (0.076) (0.078)

Hispanic 0.036 0.050 0.052 0.071(0.069) (0.071) (0.071) (0.075)

Asian −0.017 0.007 −0.009 0.021(0.106) (0.107) (0.108) (0.116)

Other Race −0.105 −0.092 −0.088 −0.081(0.093) (0.093) (0.093) (0.094)

Social class 0.054 * 0.051 0.056 * 0.072 *(0.024) (0.027) (0.027) (0.028)

Concerted Cultivation 0.012 0.019 −0.006 −0.002 0.002(0.025) (0.026) (0.028) (0.029) (0.030)

Second K. −0.174 −0.153 −0.163 −0.168 −0.151 −0.155 −0.149(0.108) (0.108) (0.108) (0.108) (0.108) (0.108) (0.109)

Changed School 0.091 * 0.085 * 0.090 * 0.096 * 0.086 m 0.090 * 0.089 *(0.045) (0.045) (0.045) (0.045) (0.045) (0.045) (0.044)

1st Grade Slope 0.638 ** 0.665 ** 0.664 ** 0.646 ** 0.664 * 0.647 ** 0.687 **(0.012) (0.010) (0.010) (0.012) (0.010) (0.012) (0.016)

Black 0.028 0.010 0.006 0.012(0.024) (0.024) (0.024) (0.025)

Hispanic 0.044 * 0.026 0.023 −0.002(0.022) (0.022) (0.023) (0.024)

Asian 0.157 ** 0.130 ** 0.138 ** 0.097 *(0.033) (0.034) (0.034) (0.036)

Other Race 0.081 ** 0.070 * 0.069 * 0.068 *(0.030) (0.030) (0.030) (0.031)

Social class −0.038 ** −0.025 ** −0.028 ** −0.036 **(0.008) (0.009) (0.009) (0.009)

Concerted Cultivation −0.040 ** −0.034 ** −0.032 ** −0.025 ** −0.023 *(0.008) (0.008) (0.009) (0.009) (0.010)

Second K. −0.025 −0.036 −0.033 −0.031 −0.038 −0.037 −0.033(0.035) (0.035) (0.035) (0.035) (0.035) (0.035) (0.035)

Changed School −0.046 −0.041 −0.045 −0.044 −0.041 −0.040 −0.025(0.035) (0.035) (0.035) (0.035) (0.035) (0.035) (0.035)

‘m’ p < .06, ‘*’ p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.a Table continued on next page.b Results and full covariate list for model G are displayed in Appendix table A.4.

96

Table 5.2 – Continued: General Knowledge AchievementModel

Variablesa A B C D E F G

Level-2 Variance ComponentsIntercept 31.094 ** 30.066 ** 28.949 ** 28.295 ** 27.851 ** 27.851 ** 25.902 **Kindergarten Slope 0.062 ** 0.062 ** 0.062 ** 0.062 ** 0.062 ** 0.062 ** 0.060 **Summer Slope 0.027 ** 0.026 ** 0.027 ** 0.028 ** 0.028 ** 0.028 ** 0.030 **

1st Grade Slope 0.038 ** 0.039 ** 0.038 ** 0.036 ** 0.037 ** 0.037 ** 0.035 **


1st Grade Slope 0.023 ** 0.022 ** 0.023 ** 0.022 ** 0.022 ** 0.022 ** 0.022 **

‘m’ p < .06, ‘*’ p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.a Results and full covariate list for model G are displayed in Appendix table A.4.

parent-child content and child experiences that Lareau (2003) observed.

When concerted cultivation is added to model A in model D, the racial group

disparities reduce significantly (26% for blacks, 28% for Hispanics, and 33% for

Asians) in magnitude, suggesting that a substantial proportion of minority group

disadvantages are related to parenting practices. Yet, the differences from whites

remain disproportionately large in all cases, clearly implicating factors other than

the parenting strategies captured by the concerted cultivation measure. The story

is similar for social class (model E) where the coefficient reduces by approximately

32%. Again, a substantial reduction, although the expected baseline difference

remains nearly 1.7 points for each standard deviation difference in social class.

As with the social group covariates, race and social class, the initial estimate of

concerted cultivation with children’s general knowledge achievement was overesti-

mated due to shared correlations with other factors. Not surprisingly, the greatest

reduction in the concerted cultivation relationships arose when social class ap-

peared in the equation (19%). Notably, however, concerted cultivation is of similar

magnitude to the social class association for the general knowledge test.

Although the race and social coefficients continue to decrease in magnitude

with additional covariates, the largest absolute reduction in all cases occurs when

concerted cultivation is entered in to the equation. Differences remain large at

model G when the full covariate list is included. Black children, after adjustments,

are the most disadvantaged group, scoring over a 12

standard deviation (3 points)

below white children (model G). Approximately half of both the Hispanic and

Asian disadvantages are due to the frequency with which these children come

from homes where a non-English language is regularly spoken. Despite reductions

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in coefficient magnitude, concerted cultivation persists as one of the strongest

predictors of children’s early school general knowledge readiness, with a larger

point estimate, in fact, than social class (1.3 versus 1.6 points, or .22 versus .27

standard deviations).

Kindergarten Slope

Coefficients for the children’s kindergarten learning rates as a function of race,

social class, and concerted cultivation are displayed in the second panel of table 5.2.

Children’s general knowledge scores increase by over .8 points per month while in

kindergarten with few systematic differences across social groups, although second

time kindergartners learn at a significantly lower rate than other children, while

those who changed schools appear to do better, on average. Amongst social groups,

the only systematic difference is a deficit of .04 points per month (about .16-.17

standard deviations) for black children which remains impervious to adjustment

by social class, concerted cultivation, and the other covariates included in model

G. The concerted cultivation measure is not systematically related to children’s

general knowledge growth during the kindergarten year.

Summer Slope

Children continue to learn over the summertime at a rate approximately half that

reported during kindergarten, which is still significant. Although previous research

has suggested learning deficits by race in the summertime (Cooper et al. 1996;

Alexander et al. 2001), no race differences are found in general knowledge using

the ECLS-K. Consistent with prior research, however, children from lower social

class families lose ground over the summer while advantaged children grow at a

substantially faster rate. These differences are moderately large. The social class

coefficient in model G, for example, is nearly 20% of the average growth rate for

all children. Another way of looking at the social class advantage/disadvantage

is to standardize the coefficient with respect to the between child variance of the

summer slope, which suggests a standardized slope of .45. This large difference is

indicative of the large social class differences in summer growth, larger than during

the school year, which led Downey et al. (2004) to recently conclude that schools

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have a tendency to reduce inequality.

There are large and meaningful differences in children’s summer learning rates

by social class; however, these differences in growth are not attributable to con-

certed cultivation which is not statistically associated with children’s growth. This

null finding is surprising as summer is the time when family influences are most

likely to be visible, and they are, but through social class and not cultural capital.

1st Grade Slope

The final panel on the first page of table 5.2 contains the parameters for growth over

first grade. Although Hispanic children appear to be growing at a faster rate than

white children (model A), this finding does not hold when additional covariates

are added to the model. The rate of general knowledge growth for Asian children,

however, is significantly higher than for white children, and the finding is robust

across model specifications. By model G, the Asian coefficient is about 14% of the

average growth rate (.69 points per month).

Higher social class children and those from homes that score higher on the

concerted cultivation measure grow more slowly over first grade, contrary to ex-

pectations. However, the difference is small (-.04 points per month for social class

and -.03 points per month for concerted cultivation, in model G) and the children

who are implied to be growing the slowest over first grade have already accumu-

lated significant advantages on the test. It must also be remembered that the

mixed growth model is attempting to estimate the average within-school growth

rate,6 which suggests that advantaged social class and concerted cultivation chil-

dren grow faster than their less advantaged peers in the same school. Because

these children are already significantly advantaged as a result of home advan-

tages, the slow growth rates may reflect within-school ceiling effects (as opposed

to test-specific ceiling effects). That is, many of these children may be “learning”

redundant information.

6In normal random effects models, this is typically not the case because, as explained inthe following section, covariates are often correlated with the random effects. The associationsestimate in these models, are in fact weighted averages of “within-” and “between-school” as-sociations. The next series of models, which leverage school-level “fixed-effects,” address the“average within-school” association more effectively.”

99

Summary

Figures summarizing race, social class, and concerted cultivation gaps are depicted

in figures 5.1 to 5.3. Figure 5.1 depicts both growth figures by race with and

without controlling for concerted cultivation in the top panel, and curves depict-

ing race/ethnic differences from whites in the bottom panel. Both sets of curves

illustrate the approximately 30% reduction in race differences at kindergarten en-

try for all groups, and highlight the black-white differences in growth over the

kindergarten year and the reducing Asian-white gap over the first grade, while the

Hispanic-white gap remains relatively constant over the study period.

Corresponding figures for social class are presented in figure 5.2. The compres-

sion of the social class gaps from the unadjusted to the adjusted figure in the top

panel is indicative of the over 30% reduction in social class coefficient magnitude,

which is also apparent in the difference curve in the bottom panel. Although chil-

dren of different social classes grow at different rates over the summer, concerted

cultivation is not implicated in the gap.

Although playing an important role in children’s initial general knowledge skills

at kindergarten entry, concerted cultivation is not strongly implicated in general

knowledge growth, as evinced by the growth and difference curves portrayed in the

top and bottom panels of figure 5.3. The largest action of concerted cultivation is

prior to kindergarten entry, and as shown in the figures, children advantaged on this

measure, somewhat surprisingly, learn at slower rates during the first grade year.

This pattern of growth, however, may be the result of within-school or classroom

ceiling effects. Even if this is the case, however, it implies that concerted cultivation

is not contributing to children’s general knowledge growth over this period.

5.2.2 Group-Mean Centered Models

Often covariates are correlated with the random effects in multilevel models, which

appears likely when considering how the level-3 variance components change across

the original model set in table 5.2. For example, adding concerted cultivation in

model C explains over 55% of the variance in the level-3 intercept, although much

smaller proportions of the variances in the slopes are explained due to variability in

average levels of concerted cultivation across schools. That the covariates might be

100

Figure 5.1. Graphical Depictions of Race Differences in Children’s General KnowledgeGrowth from Kindergarten Entry Through 1st Grade from Table 5.2

15

20

25

30

35

40

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eral

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Kindergarten Summer 1st Grade

White Black

Hispanic Asian

Race: Unadjusted (Model A)

15

20

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30

35

40

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eral

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White Black

Hispanic Asian

Race: Adjusted (Model D)

−6

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Unadjusted (Model A)

Adjusted (Model D)

Black−White General KnowledgeAchievement Difference

−6

−4

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Adjusted (Model D)

Hispanic−White General KnowledgeAchievement Difference

−6

−4

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Adjusted (Model D)

Asian−White General KnowledgeAchievement Difference

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Figure 5.2. Graphical Depictions of Social Class Differences in Children’s GeneralKnowledge Growth from Kindergarten Entry Through 1st Grade from Table 5.2

15

20

25

30

35

40

Gen

eral

Kno

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High SES (+1 sd)

Middle SES

Low SES (−1 sd)

SES: Unadjusted (Model B)

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25

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35

40

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eral

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High SES (+1 sd)

Middle SES

Low SES (−1 sd)

SES: Adjusted (Model E)

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Low SES: Unadjusted (Model B)

Low SES Adjusted (Model E)

High SES: Unadjusted (Model B)

High SES Adjusted (Model E)

SES General KnowledgeAchievement Difference

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Figure 5.3. Graphical Depictions of Differences in Children’s General KnowledgeGrowth from Kindergarten Entry Through 1st Grade by Concerted Cultivation fromTable 5.2

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20

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High C. Cult. (+1 sd)

Middle C. Cult.

Low C. Cult. (−1 sd)

Concerted Cultivation:Unadjusted (Model C)

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Middle C. Cult

Low C. Cult (−1 sd)

Concerted Cultivation:Adjusted (Model G)

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Low C. Cult: Unadjusted (Model C)

Low C. Cult Adjusted (Model G)

High C. Cult: Unadjusted (Model C)

High C. Cult Adjusted (Model G)

Concerted Cultivation: General KnowledgeAchievement Difference

103

correlated with the random effects is not surprising considering the unequal access

to educational opportunities across social groups. For this reason, the next series

of models replicate those in table 5.2 but with the covariates centered around

their respective group means. These models are, in effect, growth models with

school-level fixed-effects, which means the between-student parameters represent

average expected general knowledge scores between-children who attend the same

school.7 In addition, this approach has the added benefit of adjusting for constant

school-level confounders. Results are presented in table 5.38 and figures 5.4 to 5.6.

Initial Status

The race and social group differences in children’s general knowledge test scores

at kindergarten entry are noticeably smaller when children in the same schools are

compared, although differences are still remarkably large.9 Overall, the story por-

trayed by the non-group-mean centered and group-mean centered models is consis-

tent, however. Concerted cultivation mediates approximately 30% of the race and

social class differences in children’s initial general knowledge achievement, which is

a proportion essentially equal to that from the non-centered models. Notably, the

concerted cultivation coefficient estimate is again larger10 than that reported for

social class, indicating the importance of parenting strategies for children’s school

readiness.

The Slope Parameters

Whereas the non-group-mean centered models suggest differential growth rates

over the kindergarten year between black and white children, the results presented

in table 5.3 suggest that this relation does not hold once black and white children

in the same schools are compared. Consistent with the previous results, there

are no other statistically significant social group differences in children’s learning

7Notably, this is usually what researchers want their coefficients to represent. I present boththe group-mean centered and non-centered models because researchers are not yet entirely fa-miliar with the differences between these two approaches to model formulation.

8Partially-standardized regression slopes are reported in Appendix table A.3, coefficient mag-nitude reduction in A.1, and results for the full model are presented in table A.4.

9See Appendix table A.3 for the standardized differences.10This is merely an “eyeball” difference and is not assessed statistically.

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Table 5.3. Group-Mean Centered Growth Models for General Knowledge Achievement Scores(IRT) from Kindergarten Entry Until Spring of Third Grade by Race, Social Class, ConcertedCultivation, and Selected Covariates (ECLS-K)

Model


Initial Status 20.019 ** 20.026 ** 20.042 ** 20.040 ** 20.043 ** 20.040 ** 20.039 **(0.156) (0.156) (0.156) (0.156) (0.156) (0.156) (0.156)

Black −3.797 ** −2.745 ** −2.527 ** −2.506 **(0.234) (0.229) (0.227) (0.229)

Hispanic −3.514 ** −2.378 ** −2.098 ** −1.206 **(0.206) (0.203) (0.201) (0.206)

Asian −4.786 ** −3.186 ** −3.523 ** −1.864 **(0.289) (0.284) (0.281) (0.298)

Other Race −2.183 ** −1.470 ** −1.363 ** −1.170 **(0.296) (0.287) (0.284) (0.280)

Social class 2.106 ** 1.426 ** 1.412 ** 1.133 **(0.071) (0.073) (0.073) (0.075)


Second K. −0.818 ** −0.519 −0.637 * −0.571 * −0.395 −0.341 −0.279(0.298) (0.301) (0.291) (0.287) (0.290) (0.286) (0.288)

Age at K. Entry 0.349 ** 0.364 ** 0.357 ** 0.352 ** 0.362 ** 0.357 ** 0.356 **(0.012) (0.012) (0.012) (0.011) (0.011) (0.011) (0.011)


Black −0.016 −0.016 −0.013 −0.014(0.023) (0.023) (0.023) (0.024)

Hispanic −0.024 −0.019 −0.017 −0.021(0.020) (0.021) (0.021) (0.021)

Asian −0.011 −0.007 −0.011 −0.025(0.029) (0.029) (0.029) (0.031)

Other Race −0.019 −0.018 −0.018 −0.017(0.029) (0.029) (0.029) (0.029)

Social class 0.013 0.013 0.012 0.006(0.007) (0.008) (0.008) (0.008)

Concerted Cultivation 0.005 0.003 0.001 0.000 0.000(0.007) (0.007) (0.007) (0.008) (0.008)

Second K. −0.066 * −0.063 * −0.065 * −0.065 * −0.062 * −0.061 * −0.062 *(0.029) (0.029) (0.029) (0.029) (0.029) (0.029) (0.029)

Changed School 0.061 0.069 m 0.088 * 0.088 * 0.091 * 0.091 * 0.093 *(0.037) (0.037) (0.037) (0.037) (0.036) (0.036) (0.036)

Summer Slope 0.425 ** 0.426 ** 0.426 ** 0.427 ** 0.427 ** 0.427 ** 0.428 **(0.030) (0.030) (0.030) (0.030) (0.030) (0.030) (0.030)

Black 0.138 0.150 0.043 0.161 0.146(0.092) (0.093) (0.030) (0.093) (0.094)

Hispanic 0.018 0.033 0.036 0.058(0.081) (0.082) (0.082) (0.084)

Asian −0.023 0.004 −0.003 0.040(0.114) (0.116) (0.116) (0.123)

Other Race 0.099 0.114 0.118 0.118(0.109) (0.109) (0.109) (0.110)

Social class 0.047 0.010 0.048 0.062 *(0.028) (0.030) (0.030) (0.031)

Concerted Cultivation 0.023 0.029 −0.159 0.016 0.016(0.028) (0.029) (0.113) (0.030) (0.032)

Second K. −0.178 −0.161 −0.171 −0.173 0.054 −0.159 −0.157(0.113) (0.113) (0.113) (0.113) (0.051) (0.113) (0.114)

Changed School 0.054 0.053 0.054 0.056 0.056 0.055(0.051) (0.051) (0.051) (0.051) (0.051) (0.051)

1st Grade Slope 0.662 ** 0.662 ** 0.662 ** 0.662 ** 0.662 ** 0.662 ** 0.662 **(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)

Black 0.015 −0.001 −0.006 0.002(0.029) (0.029) (0.029) (0.030)

Hispanic 0.054 * 0.036 0.033 0.010(0.026) (0.026) (0.026) (0.027)

Asian 0.180 ** 0.152 ** 0.156 ** 0.114 **(0.036) (0.037) (0.037) (0.039)

Other Race 0.031 0.018 0.017 0.016(0.035) (0.035) (0.035) (0.035)

Social class −0.032 ** −0.020 * −0.023 * −0.030 **(0.009) (0.010) (0.010) (0.010)

Concerted Cultivation −0.043 ** −0.037 ** −0.037 ** −0.030 ** −0.027 **(0.009) (0.009) (0.009) (0.010) (0.010)

Second K. −0.022 −0.030 −0.028 −0.028 −0.033 −0.034 −0.030(0.036) (0.036) (0.036) (0.036) (0.036) (0.036) (0.036)

Changed School −0.044 −0.038 −0.042 −0.042 −0.038 −0.039 −0.025(0.036) (0.036) (0.036) (0.036) (0.036) (0.036) (0.036)

‘m’ p < .06, ‘*’ p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.a Table continued on next page.b Results and full covariate list for model G are displayed in Appendix table A.4.

105

Table 5.3 – Continued: General Knowledge Achievement, Group-Mean CenteredModel



1st Grade Slope 0.038 ** 0.039 ** 0.038 ** 0.037 ** 0.038 ** 0.037 ** 0.035 **


1st Grade Slope 0.024 ** 0.024 ** 0.024 ** 0.024 ** 0.024 ** 0.024 ** 0.024 **

‘m’ p < .06, ‘*’ p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.a Results and full covariate list for model G are displayed in Appendix table A.4.

rates over the kindergarten year, nor is concerted cultivation related to children’s

growth.

As with the kindergarten growth factor, summer results remain largely un-

changed when the growth models are estimated with school-level fixed-effects.

However, the relation of social class to children’s general knowledge acquisition

during the summertime is less consistently related. Descriptively, there is little ev-

idence that children who attend the same schools and, correspondingly, are likely

live in similar neighborhoods, grow at different rates over the summertime. In the

full model (G), however, social class emerges again as an important predictor, sug-

gesting that children otherwise equal on the covariate list who come from higher

social class family backgrounds grow at increased rates. Concerted cultivation,

in agreement with the previous models, does not appear to be related to chil-

dren’s general knowledge acquisition over the summertime, or provide systematic

information on children’s growth patterns.

The story for general knowledge growth over the first grade in table 5.3 is

based essentially on the same pattern of findings as those presented in table 5.2.

Notably, Hispanic children appear to grow more quickly in the descriptive race

model (A), but this association is in fact due to differences in the average level

of concerted cultivation Hispanic children experience. As before, Asian children

grow at a faster rate than white children, although the coefficient is slightly larger

when Asian children are compared to white children in the same schools than in

the population-based model reported in table 5.2.

The negative association between social class and concerted cultivation and

children’s growth during the first grade persists after constant, unobserved school

106

characteristics are adjusted for. Although the relationships with growth are small,

they still capture a convergence over time in children’s general knowledge.

5.2.3 Summary

The school-level fixed-effects models are consistent with the non-centered growth

models, although coefficient magnitudes tend to be reduced because the original

coefficient estimates were biased upward as a result of between-school relationships

in the group-means. The race (5.4), social class (5.5), and concerted cultivation

(5.6) figures tell stories similar to those seen previously. Although coefficients are

smaller in the centered models, the reduction in coefficient magnitude are slightly

larger than in the non-centered models, so the gaps after controlling for concerted

cultivation are slightly smaller in the figures.

As with the previous models, Asian children learn more quickly than other

children during the first grade year, which is clearly visible in both panels of figure

5.4. There is less evidence, however, of social group disparities in summertime

learning rates (see figure 5.5), although large differences emerge in the full-model.

These results are somewhat ambiguous since, in general, children of different social

class do not learn at different rates, but do once children are equalized on a sizeable

covariate list. The slower growth rates for advantaged children over the first grade

year are also visible in the figure, although the difference is not large enough

to account for the sizeable gaps that develop prior to kindergarten and over the

summertime.

The concerted cultivation growth and difference curves in figure 5.5 highlight

the general knowledge disparities at kindergarten entry and the small negative

decrement to growth rates over the first grade. These curves also illustrate the

reduction in the concerted cultivation coefficient magnitude when the full covariate

list is included in the model specification. Even after partializing the model with

a large covariate list of covariates that were shown in Chapter 4 to be related to

concerted cultivation, concerted cultivation remains amongst the most important

predictors in the model. The magnitude of the concerted cultivation and social

class decrements to first grade growth are of also of similar magnitude between

the centered and non-centered models. Thus, children in the same schools who

107

Figure 5.4. Graphical Depictions of Group-Centered Race Differences in Children’sGeneral Knowledge Growth from Kindergarten Entry Through 1st Grade from Table 5.3

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Asian−White General KnowledgeAchievement Difference

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Figure 5.5. Graphical Depictions of Group-Centered Social Class Differences in Chil-dren’s General Knowledge Growth from Kindergarten Entry Through 1st Grade fromTable 5.3

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High SES (+1 sd)

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SES General KnowledgeAchievement Difference

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Figure 5.6. Graphical Depictions of Group-Centered Differences in Children’s GeneralKnowledge Growth from Kindergarten Entry Through 1st Grade by Concerted Cultiva-tion from Table 5.3

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Middle C. Cult.


Concerted Cultivation:Unadjusted (Model C)

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Low C. Cult: Unadjusted (Model C)

Low C. Cult Adjusted (Model G)

High C. Cult: Unadjusted (Model C)

High C. Cult Adjusted (Model G)

Concerted Cultivation: General KnowledgeAchievement Difference

110

are more advantaged on these two ‘characteristics’ learn at slower rates over this

period. Notably, if the social class and concerted cultivation trends continue, then

the initial disparities between advantaged and disadvantaged children will reduce

significantly.

5.3 Interactions with Concerted Cultivation

The previous models estimated race and social class effects descriptively and after

adjusting for concerted cultivation and other covariates. Important parameters

included the race and social class coefficients across model specifications, in ad-

dition to the concerted cultivation coefficients. These models are principally con-

cerned with the (1) the extent to which concerted cultivation mediates race and

class differences, and (2) the average relationship between concerted cultivation

and children’s growth trajectories across social groups. Does concerted cultivation

function equally for groups of differing social status and race/ethnic identification?

Results are reported in table 5.4 for race interactions, table 5.5 for the social class

models, and race by class by concerted cultivation interactions are presented in

table 5.6.

5.3.1 Race x Concerted Cultivation Interactions

Results for model A in table 5.4 are estimates of race and concerted cultivation

interactions prior to controlling for social class, while model B adds social class.

Models C and D are replicates of models A and B but use group-mean centering to

eliminate unmeasured contextual factors. Consistent with the previous discussion,

the coefficients reported in models C and D are average effects based upon children

who attend the same schools.11

Because race categorization/identification is a nominal measure which has been

parsed into dichotomous indicator variables, the coefficients in table 5.4 have an

intuitive interpretation. The concerted cultivation main-effect represents the av-

11In addition to these models, I also estimated a series of equations where the sample wasrestricted to only those cases covered over the range of common support because of differencesin the distribution of concerted cultivation across race/ethnic groups. Findings are virtuallyidentical.

111

Table 5.4. Race Interactions with Concerted Cultivation Growth Models for General Knowl-edge Achievement Scores (IRT) from Kindergarten Entry Until the Spring of Third Grade (ECLS-K)

Non-Centered Centered

Variables A B C D

Initial Status 21.703 ** 21.640 ** 20.044 ** 20.043 **(0.111) (0.101) (0.156) (0.156)

Black −3.957 ** −3.660 ** −3.152 ** −2.905 **(0.207) (0.201) (0.239) (0.236)

Hispanic −2.950 ** −2.658 ** −2.250 ** −1.981 **(0.188) (0.183) (0.206) (0.204)

Asian −3.325 ** −3.851 ** −3.014 ** −3.361 **(0.278) (0.273) (0.294) (0.291)

Other −2.228 ** −2.168 ** −1.471 ** −1.369 **(0.264) (0.257) (0.288) (0.285)

Social Class 1.669 ** 1.402 **(0.068) (0.073)

Concerted Cultivation 2.640 ** 2.025 ** 2.316 ** 1.851 **(0.088) (0.091) (0.093) (0.095)

Interactions W/ C. CultivationBlack −0.983 ** −0.843 ** −0.921 ** −0.826 **

(0.180) (0.177) (0.187) (0.185)Hispanic 0.495 ** 0.519 ** 0.334 0.338

(0.177) (0.174) (0.184) (0.182)Asian 0.453 0.454 0.368 0.369

(0.259) (0.255) (0.263) (0.260)Other 0.052 0.123 −0.068 −0.044

(0.249) (0.245) (0.257) (0.254)Additional ControlsSecond Time K. −0.614 * −0.406 0.350 ** 0.356 **

(0.277) (0.275) (0.011) (0.011)Age at K. Entry 0.360 ** 0.369 ** −0.582 * −0.352

(0.011) (0.011) (0.287) (0.286)

Kindergarten Slope 0.842 ** 0.841 ** 0.823 ** 0.823 **(0.010) (0.010) (0.008) (0.008)

Black −0.035 −0.032 −0.012 −0.010(0.020) (0.020) (0.024) (0.024)

Hispanic −0.023 −0.020 −0.020 −0.018(0.018) (0.018) (0.021) (0.021)

Asian 0.004 0.002 −0.003 −0.007(0.028) (0.028) (0.030) (0.030)

Other 0.000 0.001 −0.024 −0.024(0.026) (0.026) (0.029) (0.029)

Social Class 0.010 0.013(0.007) (0.008)

Concerted Cultivation −0.010 −0.012 −0.011 −0.015(0.009) (0.009) (0.009) (0.010)

Interactions W/ C. CultivationBlack 0.044 * 0.044 * 0.035 0.035

(0.018) (0.018) (0.019) (0.019)Hispanic 0.027 0.026 0.028 0.028

(0.018) (0.018) (0.019) (0.019)Asian 0.054 * 0.055 * 0.041 0.041

(0.026) (0.026) (0.027) (0.027)Other 0.002 0.003 0.002 0.003

(0.026) (0.026) (0.027) (0.027)Additional ControlsSecond Time K. −0.071 * −0.068 * −0.064 * −0.061 *

(0.028) (0.028) (0.029) (0.029)Changed Schools 0.077 * 0.079 * 0.085 * 0.088 *

(0.036) (0.036) (0.037) (0.036)

‘m’ p < .06, ‘*’ p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.

112

Table 5.4 – Continued: General Knowledge Achievement, Race Interactions


Variables A B C D

Summer Slope 0.398 ** 0.398 ** 0.427 ** 0.428 **(0.038) (0.038) (0.030) (0.030)

Black 0.072 0.079 0.138 0.148(0.080) (0.080) (0.097) (0.097)

Hispanic 0.050 0.055 0.038 0.042(0.073) (0.073) (0.083) (0.083)

Asian 0.012 −0.005 0.017 0.011(0.114) (0.115) (0.122) (0.122)

Other −0.079 −0.076 0.126 0.130(0.094) (0.094) (0.110) (0.110)

Social Class 0.055 * 0.048(0.027) (0.030)

Concerted Cultivation 0.059 0.039 0.070 0.057(0.035) (0.037) (0.038) (0.039)

Interactions W/ C. CultivationBlack −0.120 −0.124 −0.096 −0.100

(0.075) (0.075) (0.079) (0.079)Hispanic −0.053 −0.048 −0.064 −0.061

(0.067) (0.067) (0.071) (0.071)Asian −0.073 −0.076 −0.061 −0.062

(0.104) (0.105) (0.108) (0.108)Other −0.019 −0.022 −0.072 −0.076

(0.087) (0.087) (0.091) (0.090)Additional ControlsSecond Time K. −0.169 −0.157 −0.176 −0.162

(0.108) (0.108) (0.113) (0.113)Changed Schools 0.092 * 0.086 m 0.054 0.054

(0.045) (0.045) (0.051) (0.051)

1st Grade Slope 0.657 ** 0.657 ** 0.662 ** 0.662 **(0.013) (0.013) (0.010) (0.010)

Black 0.029 0.026 0.009 0.004(0.026) (0.026) (0.031) (0.031)

Hispanic 0.026 0.023 0.034 0.031(0.023) (0.023) (0.026) (0.026)

Asian 0.114 ** 0.122 ** 0.135 ** 0.139 **(0.036) (0.036) (0.039) (0.039)

Other 0.061 * 0.061 * 0.012 0.011(0.031) (0.031) (0.035) (0.035)

Social Class −0.027 ** −0.022 *(0.009) (0.010)

Concerted Cultivation −0.062 ** −0.053 ** −0.058 ** −0.051 **(0.011) (0.012) (0.012) (0.013)

Interactions W/ C. CultivationBlack 0.088 ** 0.089 ** 0.059 * 0.059 *

(0.024) (0.024) (0.025) (0.025)Hispanic 0.046 * 0.045 * 0.033 0.032

(0.021) (0.021) (0.022) (0.022)Asian 0.018 0.019 0.002 0.002

(0.033) (0.033) (0.034) (0.034)Other 0.037 0.038 0.053 0.054


(0.035) (0.034) (0.036) (0.036)Changed Schools −0.045 −0.041 −0.042 −0.039

(0.035) (0.035) (0.036) (0.036)


113

erage expected difference in general knowledge test scores for white children who

differ by one standard deviation on the measure. The race interactions capture

the increments or decrements to the expected difference for the specific group from

whites. For example, the negative interaction coefficient for black children repre-

sents a lower return to concerted cultivation than white children experience. From

model D, the estimated return in points at kindergarten entry is (1.85-.83=) 1.02

points, compared to 1.85 points per standard deviation difference in concerted cul-

tivation for all other groups. This lower return is a perplexing finding, and one

with implications for policies utilizing the parent-child connection to ameliorate

black-white differences in children’s test scores. These results are similar to those

found by Farkas and Beron (2004)12 and Sun (1998). Although Hispanic children

appear to benefit disproportionately from the practices of concerted cultivation,

these findings do not hold for children who attend the same schools.

While black children do not appear to benefit from concerted cultivation to the

same extent as other children, there are indications that black children’s subse-

quent growth is higher, on average, when concerted cultivation is higher, than it

is for other children. Although the positive increment for growth during kinder-

garten does not persist across model specifications, black children do not face the

concerted cultivation decrement that other children do during the first grade. This

pattern of findings is surprising, so it will receive more attention in section 5.3.3

where race, social class, and concerted cultivation are interacted.

5.3.2 SES x Concerted Cultivation Interactions

In the previous section, the question of whether or not concerted cultivation relates

to children’s general knowledge achievement differently for different social groups

was asked. This section is conceptually similar, although the question is posed for

social class instead. Although social class is used as a standardized, continuous

covariate in the preceding analyses, in the following section social class is coded

as a series of dummy variables to ease interpretation. By coding social class as a

series of categorical indicators, the interpretation of the interactions is similar to

those in the race interaction models. The first grouping, low social class, captures

12They show this relation with the PPVT for social class, mother’s verbal AFQT, and cognitivestimulation (pg. 484, Table 4).

114

the bottom quartile of the distribution, average social class captures the middle

50% of the distribution, and high social class is composed of the top quartile.

Middle-class is the reference category. The results presented in table 5.5 follow a

parallel progression to those presented in table 5.4.

There are two notable findings for children’s school readiness in table 5.5. First,

the decrements associated with being low class or the increments associated with

high class families are similar in magnitude, particularly in models B and D, sug-

gesting that the association of children’s school readiness with family social class

is approximately linear in shape. Second, the interactions of social class with con-

certed cultivation are nonsignificant. According to this classification scheme, the

relationship between children’s general knowledge scores at kindergarten entry and

the extent to which parents practice concerted cultivation does not vary by social

class.

Although the results of previous analyses suggest little differentiation amongst

social groups in children’s general knowledge growth over the kindergarten year,

across all model specifications in table 5.5, children from high social class families

who practice higher levels of concerted cultivation grow at a slightly lower rate

(about 5%) during kindergarten. This finding is surprising and contrary to expec-

tations, although not very large in magnitude. No interactions by social class and

concerted cultivation are significant during the summertime. In fact, as has been

reported previously, there is little social group differentiation in children’s general

knowledge growth over the summertime, although children continue to learn.

The previous models have reported that children from higher social class fami-

lies and families that practice more concerted cultivation grow at a lower rate over

the first grade year. The results presented in table 5.5 further illustrate this fact,

and suggest that the negative social class coefficient reported previously is driven

principally by the lower growth rates of higher social class children. There is little

consistent evidence, however, that concerted cultivation has a differential relation-

ship to children’s growth by social class, although higher social class children grow

more slowly over the kindergarten year.

115

Table 5.5. Social Class Interactions with Concerted Cultivation Growth Models for GeneralKnowledge Achievement Scores (IRT) from Kindergarten Entry Until the Spring of Third Grade(ECLS-K)


Variables A B C D

Initial Status 20.254 ** 21.495 ** 20.057 ** 20.054 **(0.110) (0.112) (0.155) (0.155)

Black −3.255 ** −2.566 **(0.190) (0.227)

Hispanic −2.843 ** −2.108 **(0.180) (0.202)

Asian −3.921 ** −3.439 **(0.262) (0.282)

Other −2.173 ** −1.371 **(0.257) (0.284)

Low SES (< −1 SD) −2.156 ** −2.025 ** −1.812 ** −1.702 **(0.175) (0.174) (0.180) (0.179)

High SES (> + SD) 2.265 ** 2.321 ** 1.901 ** 1.943 **(0.164) (0.163) (0.170) (0.169)


Interactions W/ C. CultivationLow SES (< −1 SD) −0.284 −0.269 −0.284 −0.259

(0.168) (0.166) (0.172) (0.170)High SES (> + SD) 0.082 0.075 0.107 0.061

(0.163) (0.161) (0.167) (0.166)Additional ControlsSecond Time K. −0.517 −0.432 0.360 ** 0.354 **

(0.280) (0.274) (0.011) (0.011)Age at K. Entry 0.379 ** 0.368 ** −0.440 −0.385

(0.011) (0.011) (0.289) (0.285)

Kindergarten Slope 0.837 ** 0.845 ** 0.822 ** 0.822 **(0.009) (0.011) (0.008) (0.008)

Black −0.036 m −0.013 **(0.019) (0.023)

Hispanic −0.015 −0.014(0.018) (0.021)

Asian −0.011 −0.015(0.027) (0.029)

Other 0.009 −0.018(0.026) (0.029)

Low SES (< −1 SD) −0.026 −0.024 −0.024 −0.023(0.018) (0.018) (0.018) (0.018)

High SES (> + SD) 0.031 0.030 0.032 0.031(0.017) (0.017) (0.018) (0.018)


Interactions W/ C. CultivationLow SES (< −1 SD) 0.021 0.023 0.029 0.031

(0.017) (0.017) (0.018) (0.018)High SES (> + SD) −0.055 ** −0.055 ** −0.050 ** −0.050 **

(0.017) (0.017) (0.017) (0.017)Additional ControlsSecond Time K. −0.066 * −0.066 * −0.060 * −0.059 *

(0.028) (0.028) (0.029) (0.028)Changed Schools 0.079 * 0.078 * 0.086 * 0.086 *

(0.036) (0.036) (0.036) (0.036)


116

Table 5.5 – Continued: General Knowledge Achievement, SES Interactions


Variables A B C D

Summer Slope 0.419 ** 0.403 ** 0.427 ** 0.428 **(0.037) (0.042) (0.030) (0.030)

Black 0.109 0.165(0.075) (0.093)

Hispanic 0.050 0.032(0.071) (0.082)

Asian 0.005 0.005(0.108) (0.116)

Other −0.083 0.122(0.093) (0.109)

Low SES (< −1 SD) −0.056 −0.063 −0.054 −0.064(0.069) (0.070) (0.072) (0.072)

High SES (> + SD) 0.117 0.123 0.094 0.101(0.066) (0.066) (0.069) (0.070)



(0.066) (0.066) (0.068) (0.069)High SES (> + SD) −0.122 −0.126 m −0.094 −0.099


(0.108) (0.108) (0.113) (0.113)Changed Schools 0.090 * 0.094 * 0.054 0.056

(0.045) (0.045) (0.051) (0.051)

1st Grade Slope 0.688 ** 0.671 ** 0.662 ** 0.661 **(0.012) (0.014) (0.010) (0.010)

Black 0.007 −0.007(0.024) (0.030)

Hispanic 0.029 0.037(0.022) (0.026)

Asian 0.134 ** 0.153 **(0.034) (0.037)

Other 0.066 * 0.015(0.030) (0.035)

Low SES (< −1 SD) 0.008 0.009 0.006 0.008(0.022) (0.022) (0.023) (0.023)

High SES (> + SD) −0.069 ** −0.075 ** −0.053 * −0.060 **(0.021) (0.021) (0.022) (0.023)

Concerted Cultivation −0.051 ** −0.044 ** −0.049 ** −0.044 **(0.012) (0.012) (0.013) (0.013)

Interactions W/ C. CultivationLow SES (< −1 SD) 0.052 * 0.051 * 0.038 0.039

(0.021) (0.021) (0.022) (0.022)High SES (> + SD) 0.006 0.009 −0.002 0.002


(0.035) (0.034) (0.036) (0.036)Changed Schools −0.040 −0.040 −0.038 −0.039

(0.035) (0.035) (0.036) (0.036)


117

5.3.3 SES x Race x Concerted Cultivation

The next series of models, which are presented in table 5.6, estimate race by

concerted cultivation interaction models for the bottom quartile of the social class

distribution (‘A’ models), the middle-50% (‘B’ models), and the top quartile (‘C’

models) for (1) race and concerted cultivation and (2) the entire covariate list. The

previous finding that black children do not receive the same general knowledge

benefit from concerted cultivation at kindergarten entry are interesting, and a bit

perplexing too (e.g. Farkas and Beron 2004; Sun 1998). Results from table 5.6

illustrate which black children are benefitting, and which are not.

The race by concerted cultivation findings present interesting results. Both

poor and middle-class black children receive much lower returns on concerted cul-

tivation, despite the fact that poor children receive marginally higher returns on

concerted cultivation in general. Higher social class black children, however, do not

differ significantly from whites. The previous findings showing the lessened return

to concerted cultivation for black children, then, is driven by the lower return on

concerted cultivation amongst middle- and lower-class children. Importantly, the

lack of return implies that black children who are disadvantaged with regard to

concerted cultivation, do not in fact face the same consequence as other children.

In a similar vein, lower-class Asian children also report decreased returns on con-

certed cultivation, but amongst the groups more likely to have negative values on

the scale, they are also less likely to face decrements from the absence of behaviors

that comprise the concerted cultivation measure. On the other hand, middle-class

Asian children benefit disproportionately by the practices of concerted cultivation

when they come from families in the right-hand side of the distribution, but face

larger decrements when their family environment is to the left.

Higher social class children face a small decrement during kindergarten when

their parents practice concerted cultivation, although this group is likely to already

be significantly advantaged relative to other children. This finding mirrors that

found in the previous section. Concerted cultivation is not a significant predictor of

children’s general knowledge growth over the summertime, although black children

appear to face a further negative impact. The interpretation in this case, however,

is different from those previously offered. In this case, the cultivation effect for

lower-class black children is (.14-.49=) -.35, which indicates that children from

118

Table 5.6. Race & Class Interactions with Concerted Cultivation Growth Models for GeneralKnowledge Achievement Scores (IRT) from Kindergarten Entry Until the Spring of First Grade(ECLS-K)

A-1 A-2 B-1 B-2 C-1 C-2

Initial Status 19.776 ** 20.101 ** 21.490 ** 21.255 ** 24.272 ** 22.639 **(0.201) (0.400) (0.123) (0.263) (0.222) (0.541)

Black −4.282 ** −4.210 ** −3.727 ** −3.378 ** −4.669 ** −3.657 **(0.361) (0.369) (0.260) (0.262) (0.537) (0.545)

Hispanic −3.877 ** −2.643 ** −2.483 ** −1.529 ** −2.476 ** −1.345 *(0.396) (0.425) (0.238) (0.244) (0.563) (0.565)

Asian −5.599 ** −3.699 ** −2.926 ** −1.315 ** −4.442 ** −2.859 **(0.913) (0.939) (0.431) (0.448) (0.489) (0.541)

Other −4.201 ** −3.930 ** −2.168 ** −1.692 ** −2.733 ** −2.202 **(0.542) (0.533) (0.345) (0.336) (0.690) (0.678)

Social Class 0.198 1.377 ** 1.342 **(0.167) (0.210) (0.215)

Concerted Cultivation 2.369 ** 1.939 ** 2.166 ** 1.686 ** 2.042 ** 1.756 **(0.227) (0.232) (0.128) (0.129) (0.189) (0.193)

Interactions W/ C. CultivationBlack −1.146 ** −0.932 ** −1.038 ** −1.021 ** 0.508 0.272

(0.321) (0.320) (0.282) (0.273) (0.563) (0.550)Hispanic −0.261 −0.345 0.388 −0.168 0.437 −0.123

(0.356) (0.354) (0.274) (0.274) (0.616) (0.604)Asian −1.451 * −1.247 m 1.498 ** 0.917 * 0.407 0.129

(0.660) (0.649) (0.429) (0.425) (0.502) (0.495)Other −1.175 * −1.122 * 0.235 0.153 1.360 * 0.941

(0.510) (0.504) (0.376) (0.365) (0.629) (0.618)

Kindergarten Slope 0.820 ** 0.825 ** 0.852 ** 0.866 ** 0.887 ** 0.908 **(0.023) (0.037) (0.012) (0.017) (0.020) (0.037)

Black −0.065 −0.078 −0.017 −0.015 −0.060 −0.080(0.041) (0.043) (0.026) (0.027) (0.051) (0.053)

Hispanic −0.064 −0.033 −0.021 −0.032 −0.074 −0.100(0.046) (0.049) (0.024) (0.025) (0.054) (0.056)

Asian −0.005 0.055 −0.051 −0.088 0.039 0.004(0.107) (0.111) (0.044) (0.047) (0.048) (0.054)

Other 0.069 0.067 −0.031 −0.032 −0.012 −0.023(0.062) (0.062) (0.034) (0.034) (0.067) (0.067)

Social Class −0.020 0.026 −0.005(0.019) (0.022) (0.021)

Concerted Cultivation 0.047 0.046 −0.008 −0.008 −0.050 ** −0.042 *(0.026) (0.026) (0.013) (0.013) (0.018) (0.019)

Interactions W/ C. CultivationBlack −0.010 −0.012 0.044 0.040 −0.024 −0.026

(0.037) (0.037) (0.029) (0.029) (0.055) (0.056)Hispanic −0.038 −0.045 0.034 0.044 0.093 0.103

(0.042) (0.042) (0.027) (0.028) (0.061) (0.061)Asian 0.012 0.006 0.075 0.084 0.057 0.075

(0.077) (0.077) (0.047) (0.048) (0.051) (0.052)Other −0.019 −0.022 0.007 0.007 0.031 0.036

(0.059) (0.059) (0.038) (0.038) (0.061) (0.061)

‘m’ p < .06, ‘*’p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.Note 1: Table continued on the following page.Note 2: ‘A’ models are for the lower quantile of the social class distribution, ‘B’ models are estimated from themiddle 50% of the distribution, and ‘C’ models use the top quantile of the social class distribution. For theconcerted cultivation distribution by race and social class, see figure 4Note 3: ‘-1’ models include second time kindergartner, age at kindergarten entry (-66 months), and whether ornot the child changed schools over the given period. ‘-2’ models include the full covariate list.

119

Table 5.6 – Continued: General Knowledge Achievement, Race & Social Class Interactions

A-1 A-2 B-1 B-2 C-1 C-2

Summer Slope 0.393 ** 0.503 ** 0.416 ** 0.409 ** 0.470 ** 0.283 *(0.097) (0.145) (0.046) (0.063) (0.078) (0.137)

Black −0.128 −0.075 −0.034 −0.062 0.395 * 0.344(0.177) (0.180) (0.103) (0.105) (0.199) (0.204)

Hispanic 0.033 0.149 0.064 0.094 0.110 0.060(0.181) (0.190) (0.096) (0.101) (0.182) (0.189)

Asian 0.061 0.335 −0.051 0.002 0.178 0.071(0.472) (0.480) (0.185) (0.193) (0.181) (0.200)

Other −0.261 −0.186 −0.104 −0.095 0.047 0.047(0.209) (0.211) (0.125) (0.126) (0.244) (0.245)

Social Class 0.163 * −0.029 0.050(0.081) (0.088) (0.079)

Concerted Cultivation 0.174 0.163 0.075 0.088 −0.037 −0.063(0.105) (0.107) (0.051) (0.054) (0.069) (0.072)

Interactions W/ C. CultivationBlack −0.494 ** −0.497 ** −0.039 −0.040 −0.055 −0.015

(0.158) (0.158) (0.119) (0.120) (0.213) (0.214)Hispanic −0.156 −0.181 −0.053 −0.077 −0.129 −0.096

(0.155) (0.156) (0.108) (0.110) (0.199) (0.201)Asian −0.033 0.040 −0.137 −0.181 −0.249 −0.267

(0.333) (0.326) (0.194) (0.196) (0.189) (0.191)Other −0.180 −0.178 −0.059 −0.066 −0.109 −0.130

(0.192) (0.192) (0.134) (0.134) (0.225) (0.226)

1st Grade Slope 0.680 ** 0.744 ** 0.677 ** 0.700 ** 0.585 ** 0.655 **(0.032) (0.046) (0.016) (0.021) (0.026) (0.044)

Black 0.023 0.024 0.062 0.067 m −0.004 0.006(0.056) (0.057) (0.034) (0.034) (0.063) (0.065)

Hispanic 0.051 0.005 0.021 −0.008 0.082 0.077(0.055) (0.059) (0.031) (0.033) (0.057) (0.059)

Asian 0.048 −0.071 0.135 * 0.085 0.136 * 0.124 *(0.149) (0.153) (0.059) (0.061) (0.057) (0.062)

Other 0.120 0.109 0.085 * 0.077 0.048 0.046(0.069) (0.070) (0.041) (0.042) (0.079) (0.079)

Social Class −0.014 −0.021 −0.036(0.025) (0.029) (0.025)

Concerted Cultivation −0.057 −0.064 m −0.069 ** −0.068 ** −0.023 −0.013(0.033) (0.034) (0.017) (0.018) (0.022) (0.023)

Interactions W/ C. CultivationBlack 0.122 ** 0.126 * 0.089 * 0.086 * 0.044 0.035

(0.051) (0.051) (0.038) (0.038) (0.067) (0.067)Hispanic 0.082 0.104 * 0.044 0.066 −0.083 −0.085

(0.047) (0.048) (0.035) (0.035) (0.062) (0.063)Asian 0.007 −0.006 0.042 0.062 −0.005 0.010

(0.106) (0.105) (0.061) (0.062) (0.057) (0.058)Other 0.103 0.101 0.030 0.031 0.004 0.015

(0.067) (0.068) (0.044) (0.044) (0.075) (0.075)

‘m’ p < .06, ‘*’p < .05, ‘**’ p < .01 Note: Standard errors are in parentheses.Note 1: ‘A’ models are for the lower quantile of the social class distribution, ‘B’ models are estimated from themiddle 50% of the distribution, and ‘C’ models use the top quantile of the social class distribution. For theconcerted cultivation distribution by race and social class, see figure 4Note 2: ‘-1’ models include second time kindergartner, age at kindergarten entry (-66 months), and whether ornot the child changed schools over the given period. ‘-2’ models include the full covariate list.

120

negative-valued families are growing at faster rates than children from families in

the positive part of the distribution, who face the deficit. Trends for black children

do a reversal, however, during the first grade year. Whereas white lower- and

middle-class children (it is these children driving the negative concerted cultivation

coefficient in previous analyses) grow more slowly for higher levels of concerted

cultivation, this negative relationship is null or slightly positive for black children

in those socioeconomic strata.

These findings are complex and suggest that in some cases, black children

suffer from fewer negative impacts relative to advantaged concerted cultivation

children, primarily prior to school and during the summertime. Given the social

class graded nature of the race-specific concerted cultivation distributions, the

data do not support strong hypotheses about how lower-class black children are

expected to benefit from high levels of concerted cultivation.

5.4 Chapter Summary

The pattern of results is more confusing and difficult to interpret than the ini-

tial models in table 5.1 suggested when race by concerted cultivation interactions

are included in the models. Overall, however, concerted cultivation is amongst

the most powerful predictors of children’s general knowledge school readiness em-

ployed in the analysis. Children from families that practice high levels of concerted

cultivation have significantly more knowledge about the world, as measured by the

test, than other children. Perhaps somewhat surprisingly, this relationship is even

stronger than that for social class, although racial/ethnic deficits remain trou-

blingly large, particularly for black children, even with the full covariate list is

included in the model. Children by social group do not have consistently differ-

entiated growth rates, and in fact, there is a small tendency towards convergence

over time for all social groups. The lack of differentiation, in turn, means that

variances around growth parameters are generally small.

The results, once race and social class interactions with concerted cultivation

are taken into consideration, particularly for black children, are puzzling. On the

surface, the results in table 5.4 indicate that black children receive fewer returns

on concerted cultivation than other children. Perhaps a statistical answer lies in

121

the consideration of table 4.5 and figures 4, 4.5, and 4. Given the distribution of

concerted cultivation across lower-class black families, it seems that many black

children are actually not doing as poorly as we would expect if the children were

white, aside from the main-effect disparity. Lower social class Asian children also

showed a much more constrained relationship with concerted cultivation, although

middle-class children experienced a magnification of the concerted cultivation as-

sociation.

The magnitude of the concerted cultivation and social class decrements to first

grade growth are also of similar magnitude between the centered and non-centered

models. Children in the same schools who are more advantaged on these two

‘characteristics’ learn at slower rates over this period. Parents may contribute to

general knowledge growth less over time as the importance of concerted cultivation

transitions from being cognitive to social and psychological in nature (e.g., chil-

dren’s emerging sense of entitlement versus constraint). In conjunction, it is also

possible that advantaged children learn relatively less in school since teachers must

teach to a distribution and many of these children have already accrued significant

advantages. If this is the case, many advantaged children acquire redundant gen-

eral knowledge during the first grade, which leads to slightly smaller overall growth

rates during this period. In addition, there may be a divergence in the sorts of

knowledge children acquire from their parents and those items administered on the

general knowledge test.

CHAPTER

SIX

Math Achievement

Mathematics instruction is facing increasing scrutiny as international data such

as the Trends in International Mathematics and Science Study (TIMSS, formerly

known as the Third International Mathematics and Science Study) show U.S. chil-

dren underperforming relative to children in other Western, industrialized coun-

tries (Gonzales, Guzmn, Partelow, Pahlke, Jocelyn, Kastberg, and Williams 2004).

Larger market forces are also driving interest in mathematics education as technol-

ogy industry leaders bemoan the paucity of mathematics skills children leave school

with. At a time when the U.S. populace expresses increasing concern over the ‘out-

sourcing’ of jobs, the inability of the education system to produce workers with

the necessary skills for high-paying market segments presents a troubling problem

for policy makers and business leaders. Not surprisingly, improving mathematics

achievement is one of the principal aims of No Child Left Behind (NCLB).

Within this broad social issue, it is worth asking what role the family plays

in children’s mathematics skill development. Children begin school with large

differences in math knowledge (Lee and Burkham 2003) and grow at different rates

during school (Downey et al. 2004; Phillips et al. 1998b). What is the pattern

of change for mathematics achievement from kindergarten entry through the third

grade for different social groups, and what role does concerted cultivation play in

this process?

123

6.1 Math Achievement Growth

Results for two descriptive, baseline growth equations are presented in table 6.1

for baseline comparisons with later models. The growth parameters are arrayed

across the columns, rather than down the rows as is typical. The first uncondi-

tional model includes no control variables, while the second conditional baseline

model includes covariates for age at kindergarten entry, whether the child is a

second-time kindergartner, and whether the child changed schools over a given

period—covariates which are included in all subsequent models. Children begin

school with broadly varying math skills, with a mean of 18 points and a standard

deviation of (√

52.3 =) 7.2 points. In addition, children appear to grow between

2 and 5 times faster over the school year than during the summer when they are

out of school, although even then children, on average, acquire about .5 points per

month.1 There is also a notable difference in the variance components associated

with the growth parameters. Variability in summer slopes is far larger than for

the other parameters, being (√

4.4 =) 2.1 standard deviations—which indicates

substantial gains for some children and losses for others, compared to (√

.17 =)

.41 standard deviations for the second and third grade slope, which has the small-

est variance. Both mean differences in the growth parameters and differences in

parameter variability were key reasons that led Downey and colleagues (2004) to

conclude that schools have an equalizing influence on children’s academic compe-

tencies.

The between-student correlations between the growth parameters and initial

status provide little evidence that children who begin school with differing math-

ematics knowledge learn at systematically different rates in subsequent periods

than periods in the same schools, although initially advantaged children appear to

learn more over the summertime than their peers. Children who learn more over

the summer, however, tend to have learned at lower rates over the kindergarten

year and also had lower learning rates in the first grade than children in the same

schools. In addition, children who learned faster during the first grade were likely

to have had reduced growth rates over the second and third grade period.

1Note that the second and third grade slope is not really directly comparable to the kinder-garten, summer, and first grade slopes because the second third grade slope is a composite oftwo summer and two school year trends.

124

Table 6.1. Null Growth Models for Math Achievement Scores (IRT) from KindergartenEntry Until Spring of Third Grade

Intercept Kindergarten Summer 1st 2nd − 3rd

Null Model (w/out Covariates)Growth Parameters 18.067 1.655 0.533 2.402 1.193

(0.153) (0.014) (0.055) (0.022) (0.007)

L-2 Random Effects1

Intercept 52.267Kindergarten 0.018 0.602Summer 0.194 −0.325 4.4141st 0.075 0.125 −0.345 1.0012nd − 3rd −0.021 0.058 0.022 −0.363 0.173

L-3 Random Effects1

Intercept 17.205Kindergarten 0.343 0.112Summer 0.142 −0.398 0.5231st 0.262 0.271 −0.346 0.1592nd − 3rd 0.192 −0.174 0.292 −0.286 0.025

Null Model (w/ Covariates)Growth Parameters 18.544 1.664 0.531 2.410 1.199

(0.149) (0.014) (0.056) (0.022) (0.007)Second Time K. −1.566 −0.183 −0.156 −0.203 −0.103

(0.377) (0.046) (0.208) (0.076) (0.024)Age at K. Entry 0.496

(0.017)Changed Schools −0.138 0.180 −0.137 −0.016

(0.075) (0.100) (0.083) (0.011)

L-2 Random Effects1

Intercept 49.070Kindergarten 0.009 0.601Summer 0.198 −0.327 4.4011st 0.083 0.124 −0.345 1.0012nd − 3rd 0.004 0.056 0.021 −0.364 0.172

L-3 Random Effects1

Intercept 15.655Kindergarten 0.270 0.111Summer 0.171 −0.400 0.5341st 0.223 0.264 −0.353 0.1572nd − 3rd 0.240 −0.180 0.279 −0.293 0.025


125

The results presented in table 6.1 also demonstrate significant between school

variability in the mean growth parameter levels. Although the correlations between

initial status and the other growth parameters in the between-student model were

low, average growth rates of schools with higher average initial achievement appear

are faster during all periods. Despite the fact that children from schools with higher

than average learning rates in the summer learned less over the kindergarten and

first grade years, these children learned at a faster rate over the second and third

grade years. Although the between-student correlations are not reflective of a

Matthew effect within school, the positive between-school correlations between

initial status and the slopes suggests that schools with higher average levels of

initial achievement have higher than average growth rates.

The results of the initial, baseline growth model highlight that:

1. There is substantial between-child variability both in children’s math skillswhen they enter school and in the temporal patterns of change.

2. Children learn more math skills within than outside of school, on average,although some of the highest period-specific growth rates (and correspond-ingly, some of the lowest) occur over the summertime.

3. Children’s initial skills are not strongly related to change in the within-schoolbetween-student model, although children in schools with higher averagelevels of achievement tend to have higher average growth rates.


Parameter estimates for descriptive and explanatory three-level growth models are

presented in table 6.2. In addition, coefficient reduction summaries and standard-

ized coefficients are available in Appendix tables A.5 and A.6. All coefficients for

equations A through F are reported in the table while the full model results for

model G are available in Appendix table A.8 (along with a series of intermediate

models)2. Models A through C are descriptive in nature, estimating what is essen-

tially the “bivariate” relationship between race (A), social class (B), and concerted

cultivation (C) on children’s general knowledge trajectories. The next model set

in the series, D and E, adjusts the race (D) and social class differences (E) for


126

differences in concerted cultivation levels. Race, social class, and concerted culti-

vation are included simultaneously in model F, while the full control list is entered

into the equation in model G.3 All models adjust for whether or not the child is a

second time kindergartner, age at kindergarten entry, and whether or not the child

moved during any given period, per the second null model presented in table 6.1.

Initial Status

From columns A and B of table 6.2, it is clear that social group status is signif-

icantly related to children’s early math skills. Black children score, on average,

about 3.3 points or .45 standard deviations lower than white children at kinder-

garten entry, and the disparity is even larger for Hispanic children (4 points or .55

standard deviations). Asian children, however, are not statistically distinguishable

from white children. The social class difference is also large, 2.9 points which is

an effect size of .4. These disparities are large in magnitude and imply significant

mathematical skill disparities at kindergarten entry, long before the formal educa-

tional system becomes implicated in children’s general academic development.

The growth parameters are allowed to vary with concerted cultivation in model

C. The association between initial status and concerted cultivation is nearly as large

as that for social class, over 2.5 points, which is an effect size of .35. Depending

upon the parenting strategies employed, children begin school with clearly differ-

entiated mathematics skills. Two children differing by two standard deviations

of concerted cultivation are expected to begin kindergarten with a 5 point or .7

standard deviation disparity.

The black and Hispanic coefficients are attenuated when concerted cultivation is

added to model D by approximately 40%. Together social class and concerted cul-

tivation explain nearly 60% and 55% of the black and Hispanic gaps, respectively.

By model F the black deficit has decreased to 1.3 points (.18 standard deviations),

while the Hispanic decrement has fallen to 1.8 points (.25 standard deviations).

Given that concerted cultivation is a mediator of social class and sociodemographic

associations, it is safe to say that concerted cultivation is the largest mediator of

race differences included in the models. As noted previously, Asian children’s math

scores at kindergarten entry are not differentiable from whites’. When concerted

3Results and full covariate list for model G are displayed in Appendix table A.8.

127

Table 6.2. Growth Models for Math Achievement Scores (IRT) from Kindergarten Entry UntilSpring of Third Grade by Race, Social Class, Concerted Cultivation, and Selected Covariates(ECLS-K)

Model


Initial Status 19.833 ** 18.505 ** 18.504 ** 19.172 ** 18.476 ** 18.970 ** 18.065 **(0.153) (0.109) (0.118) (0.135) (0.100) (0.122) (0.275)

Black −3.281 ** −1.892 ** −1.320 ** −1.047 **(0.265) (0.259) (0.249) (0.256)

Hispanic −4.007 ** −2.443 ** −1.809 ** −1.554 **(0.225) (0.226) (0.218) (0.239)

Asian −0.041 1.808 ** 1.391 ** 1.659 **(0.347) (0.340) (0.333) (0.357)

Other Race −2.203 ** −1.544 ** −1.418 ** −1.175 **(0.351) (0.340) (0.328) (0.324)

Social class 2.913 ** 2.234 ** 2.069 ** 1.685 **(0.079) (0.086) (0.087) (0.092)


Second K. −1.296 ** −1.025 ** −1.120 ** −0.947 ** −0.821 * −0.710 * −0.592(0.368) (0.363) (0.368) (0.360) (0.358) (0.353) (0.354)

Age at K. Entry 0.482 ** 0.506 ** 0.489 ** 0.479 ** 0.496 ** 0.486 ** 0.489 **(0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016)


Black −0.329 ** −0.272 ** −0.249 ** −0.243 **(0.032) (0.033) (0.033) (0.034)

Hispanic −0.186 ** −0.122 ** −0.098 ** −0.078 *(0.027) (0.029) (0.029) (0.031)

Asian −0.069 0.011 −0.005 0.028(0.043) (0.044) (0.044) (0.047)

Other Race −0.126 ** −0.095 * −0.085 * −0.075(0.043) (0.043) (0.043) (0.043)

Social class 0.120 ** 0.091 ** 0.080 ** 0.064 **(0.010) (0.011) (0.011) (0.012)


Second K. −0.173 ** −0.155 ** −0.167 ** −0.162 ** −0.151 ** −0.149 ** −0.145 **(0.046) (0.046) (0.046) (0.046) (0.046) (0.046) (0.046)

Changed School −0.125 −0.116 −0.082 −0.075 −0.080 −0.074 −0.060(0.074) (0.074) (0.074) (0.074) (0.073) (0.073) (0.073)

Summer Slope 0.531 ** 0.527 ** 0.529 ** 0.495 ** 0.525 ** 0.485 ** 0.397 **(0.069) (0.056) (0.056) (0.070) (0.056) (0.070) (0.095)

Black −0.091 −0.015 0.012 0.014(0.137) (0.141) (0.142) (0.146)

Hispanic −0.019 0.064 0.092 0.053(0.124) (0.130) (0.131) (0.143)

Asian 0.513 * 0.621 ** 0.611 ** 0.549 *(0.201) (0.205) (0.205) (0.218)

Other Race −0.064 −0.024 −0.034 −0.039(0.177) (0.179) (0.179) (0.180)

Social class 0.145 ** 0.110 * 0.097 0.099(0.045) (0.051) (0.051) (0.053)

Concerted Cultivation 0.096 * 0.115 * 0.063 0.085 0.090(0.044) (0.048) (0.050) (0.052) (0.055)

Second K. −0.152 −0.133 −0.138 −0.132 −0.123 −0.122 −0.138(0.208) (0.209) (0.208) (0.208) (0.208) (0.208) (0.208)

Changed School 0.183 0.147 0.191 0.194 * 0.157 0.159 0.180(0.099) (0.099) (0.099) (0.099) (0.099) (0.098) (0.098)

1st Grade Slope 2.521 ** 2.410 ** 2.409 ** 2.499 ** 2.409 ** 2.493 ** 2.598 **(0.026) (0.022) (0.021) (0.027) (0.021) (0.027) (0.034)

Black −0.341 ** −0.294 ** −0.275 ** −0.257 **(0.051) (0.052) (0.053) (0.054)

Hispanic −0.192 ** −0.140 ** −0.119 ** −0.131 *(0.045) (0.047) (0.047) (0.052)

Asian −0.351 ** −0.290 ** −0.306 ** −0.323 **(0.071) (0.073) (0.073) (0.078)

Other Race −0.242 ** −0.221 ** −0.208 ** −0.194 **(0.067) (0.068) (0.068) (0.068)

Social class 0.096 ** 0.070 ** 0.066 ** 0.043 *(0.016) (0.018) (0.019) (0.019)

Concerted Cultivation 0.102 ** 0.073 ** 0.072 ** 0.045 ** 0.044 *(0.016) (0.017) (0.018) (0.019) (0.020)

Second K. −0.195 * −0.182 * −0.188 * −0.186 * −0.177 * −0.177 * −0.171 *(0.076) (0.076) (0.076) (0.076) (0.076) (0.076) (0.076)

Changed School −0.137 −0.114 −0.132 −0.131 −0.112 −0.113 −0.088(0.083) (0.083) (0.083) (0.083) (0.083) (0.083) (0.082)

‘m’ p < .06, ‘*’ p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.a Table continued on next page.b Results and full covariate list for model G are displayed in Appendix A.8.

128

Table 6.2 – Continued: Math AchievementModel


2nd − 3rd Grade Slope 1.213 ** 1.199 ** 1.199 ** 1.211 ** 1.199 ** 1.207 ** 1.251 **(0.008) (0.007) (0.007) (0.008) (0.007) (0.008) (0.011)

Black −0.123 ** −0.119 ** −0.108 ** −0.099 **(0.016) (0.016) (0.017) (0.017)

Hispanic −0.012 −0.007 0.004 −0.005(0.014) (0.014) (0.015) (0.016)

Asian 0.079 ** 0.086 ** 0.079 ** 0.074 **(0.021) (0.021) (0.021) (0.023)

Other Race −0.004 −0.003 −0.001 0.006(0.022) (0.022) (0.022) (0.022)

Social class 0.038 ** 0.040 ** 0.035 ** 0.027 **(0.005) (0.006) (0.006) (0.006)

Concerted Cultivation 0.010 * 0.009 −0.005 −0.004 −0.004(0.005) (0.005) (0.006) (0.006) (0.006)

Second K. −0.100 ** −0.095 ** −0.102 ** −0.099 ** −0.095 ** −0.094 ** −0.091 **(0.024) (0.024) (0.024) (0.024) (0.024) (0.024) (0.024)

Changed School −0.012 −0.016 −0.014 −0.011 −0.015 −0.012 −0.012(0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011)


1st Grade Slope 0.995 ** 0.995 ** 0.998 ** 0.993 ** 0.994 ** 0.989 ** 0.982 **

2nd − 3rd Grade Slope 0.171 ** 0.172 ** 0.172 ** 0.171 ** 0.172 ** 0.171 ** 0.169 **


1st Grade Slope 0.138 ** 0.151 ** 0.144 ** 0.134 ** 0.146 ** 0.136 ** 0.137 **

2nd − 3rd Grade Slope 0.023 ** 0.023 ** 0.024 ** 0.023 ** 0.023 ** 0.022 ** 0.022 **

‘m’ p < .06, ‘*’ p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.a Results and full covariate list for model G are displayed in Appendix A.8.

cultivation is added to the model, however, Asian children are significantly ad-

vantaged, over 1.8 points or .25 standard deviations. This finding indicates that

Asian children who come from homes with the same levels of concerted cultivation

as white children are doing significantly better.

The reduction in the social class coefficient is somewhat smaller than for black

and Hispanic children, even though it is over 20%. The full covariate list is added

in model G. Although the race, social class, and concerted cultivation coefficients

are further attenuated, they all remain statistically significant predictors. While

the magnitude of the concerted cultivation coefficient is smaller than that for social

class, they are of a similar magnitude, with social class implying an effect size over

.2 and concerted cultivation slightly below that, indicating meaningful residual

associations with children’s math skills at kindergarten entry. After adjusting for

the full covariate list, social class and concerted cultivation are more powerful

predictors of children’s mathematics school readiness than race.

129

The Kindergarten Slope

The next panel in table 6.2 reports how child characteristics relate to systematic

change in children’s general knowledge over the kindergarten year. Coefficients

are in math points per month. The null model presented in 6.1 suggests that

the between-child standard deviation in growth rates is about (√

.6 =) .77, sug-

gesting substantial heterogeneity in growth rates over this period. The results

for model A indicate that black children grow more slowly than white children

over the kindergarten year, acquiring .33 points per month fewer (about .42 stan-

dard deviations). Hispanic children also grow at a slower rate, nearly .19 points

per month, while Asian children grow at approximately the same rates as white

children. Although these associations are attenuated when concerted cultivation

is added to the model (model D), the black disadvantage remains large (a 17%

reduction), indicating that differences from white children’s mathematics learning

rates during kindergarten result from factors other than concerted cultivation. The

Hispanic difference, however, is reduced by nearly 34%. Unlike initial status, the

Asian coefficient does not become significant when concerted cultivation is added

to the model. Race thus remains an important predictor of children’s learning

rates across model specifications.

Social class in model B is significantly associated with children’s learning rates,

with children one standard deviation above the mean accruing, on average, an

additional .12 points per month. The modest social class coefficient is reduced by

approximately 25% when concerted cultivation is added to the equation (model

E), but remains a significant predictor (.09 points per month). Although the as-

sociation is small by model G, social class continues to systematically differentiate

children’s learning rates.

Concerted cultivation is a significant predictor of children’s growth as well, the

coefficient magnitude in equation C similar to that for social class (in model B).

Although concerted cultivation reduces in models D and E, it remains a significant

predictor of children’s learning rates over the kindergarten year, even when the full

covariate list is added in model G. Given Children’s large growth rate, nearly 1.8

points per month (model G), school related factors appear to be the most impor-

tant determinants of children’s growth, although it is worth noting that, with the

strong correlation between social class and concerted cultivation, children’s growth

130

rates are meaningfully differentiated by these two characteristics. The black-white

difference is also large, indicating a rapid divergence from white children in test

scores over the kindergarten year.

The Summer Slope

Although children continue learning over the summertime, the overall rate is much

smaller than over kindergarten and the other grades, but these rates are signifi-

cantly more variable. The only consistent effect across models is a large, significant

Asian advantage. For reasons that are not captured in the model, Asian children

grow over the summertime at rates over twice as large as those for children in other

social groups with comparable backgrounds.

Despite previous research which reports important race and class differences in

children’s summertime learning (e.g. Alexander et al. 2001; Cooper et al. 1996),

black and Hispanic children in this data set learn at the same rates as whites, and

the same holds true for children of different social classes once additional covariates

are added to the model. Furthermore, the covariates used in the analysis do not

explain the large variance associated with summertime slopes.

These results are somewhat surprising, particularly for the lack of consistent

association between growth rates and either social class or concerted cultivation.

Children at this age are still learning relatively simple mathematical operations,

such as counting and simple addition, so presumably there is a good deal to be

offered by the question-answer based practices of concerted cultivation and the life

experiences accrued by these children. However, it is important to note that the

conversational element of concerted cultivation is only captured indirectly with the

measure used. Yet, the lack of a systematic relationship is surprising, particularly

when children, on average, continue developing their early math skills over this

period.

The 1st Grade Slope

When children enter school again in the first grade, black (-.34 points per month)

and Hispanic (-.19 points per month) children resume learning at much slower

rates. Asian children also learn slower, at the lowest rate (-.36 points per month),

131

in fact. Given that the standard deviation of the first grade slope is approximately

one, these differences represent meaningful disparities in children’s first grade math

learning. These differences remain consistent across model specifications, although

concerted cultivation explains about 27% of the Hispanic disadvantage and approx-

imately 15% of the black and Asian associations reported in model A. Nonetheless,

whites remain significantly advantaged when to other groups over this period.

Although social class is a significant predictor across specifications, as a pre-

dictor the influence it exercises is relatively small, decreasing from .1 (model B) to

.04 (model G). Concerted cultivation individually contributes a 27% reduction to

the social class association. The story for concerted cultivation is essentially the

same. While these effects are small, particularly as a fraction of the overall growth

rate, it must be remembered that even small differences can become quite large

over time. Furthermore, the magnitude of the concerted cultivation effect is nearly

identical to that for social class. As with growth during the kindergarten year,

however, race is a more salient predictor of systematic learning differentials than

either social class or concerted cultivation, although concerted cultivation explains

modest proportions of the racial/ethnic differentials.

The 2nd − 3rd Grade Slope

Black children learn less over the course of the second and third grade than white

children, while Asian children learn at a faster rate than their peers. Neither

association is importantly mediated by concerted cultivation or social class. While

social class is a significant predictor, the effect size is small, although over time

even small influences can produce large differences. Concerted cultivation is not

associated with the math growth rates of children over this time period.

Summary

The results shown in the previous sections are graphically depicted in figures 6.1

to 6.6. Growth curves by race are presented in figure 6.1 and the differences from

whites are shown in figure 6.2. For race, the largest impacts of concerted cultivation

are on the initial differences when children enter kindergarten, which is most clearly

evident in figure 6.2, although small reductions in the differences in the growth

132

rates are evidenced in both figures. Without adjusting for concerted cultivation,

the difference between black and white children at the end of third grade is expected

to be 12.6 points or .8 standard deviations,4 a doubling of the standardized black-

white difference, but is reduced to 9.9 points or .63 standard deviations when

concerted cultivation is adjusted for (a 21% reduction). The Hispanic difference

drops from 7.8 to 4.8 points (a 38% reduction), and differences in growth stabilize

over the second and third grade years, while Asians score similarly to whites at

the end of the study period. Importantly, the figures show that black and white

children’s test scores diverge over time, with even larger differences between Asian

and black children, while there is less evidence for Hispanic-white divergence. If

the trends in the figures continue, Asian children are expected to diverge from

children of other race/ethnic groups as they age through the education system,

whereas black children will fall ever farther behind (e.g. Phillips et al. 1998b).

Growth and difference curves for social class plotted at the mean and ±1 stan-

dard deviation are presented in figures 6.3 and 6.4. As suggested by the coefficients

reported in table 6.2, social class differences in growth rates are small, although

there are large social class differences at kindergarten entry. Even though the so-

cial class differences persist, concerted cultivation explains nearly one-quarter of

the social class effect at kindergarten entry, but only about 20% at the end of

third grade (6.3 point difference versus 5.0 points) due to the temporal divergence

in growth shown in figure 6.3 and the differences shown in figure 6.4. Although

advantaged and disadvantaged children diverge over time, the magnitude of the

differences is not as large as that for black children, unless children in the far ends

of the tails are compared. Yet, at the rates estimated with these data, differences

will be substantial by the time these children reach high school.

The final figures, 6.5 and 6.6, plot the growth and difference curves for con-

certed cultivation, comparing children from average and ±1 standard deviation

families. Although there is evidence for divergence through the first grade, the

differences are markedly reduced with the inclusion of the full-covariate list. Dif-

ferences, however, remain meaningful and highlight the importance of parenting

strategies in children’s early academic processes. There is little change in achieve-

4Estimated from models with the time variables recoded so that the model intercept appliesto the end of third grade. The standard deviations of the intercept is 15.7. These results are notshown.

133

Figure 6.1. Graphical Depictions of Children’s Math Growth from Kindergarten EntryThrough 3rd Grade by Race from Table 6.2

15

25

35

45

55

65

75

85

95

Mat

h A

chie

vem

ent


White Black

Hispanic Asian


15

25

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85

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Mat

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White Black

Hispanic Asian


134

Figure 6.2. Graphical Depictions of Race Differences from Whites’ in Children’s MathGrowth from Kindergarten Entry Through 3rd Grade from Table 6.2

420

−2−4−6−8

−10−12−14A

chie

vem

ent D

iffer

ence



Adjusted (Model D)

Black−White Math Achievement Difference

420

−2−4−6−8

−10−12−14A

chie

vem

ent D

iffer

ence



Adjusted (Model D)

Hispanic−White Math Achievement Difference

420

−2−4−6−8

−10−12−14A

chie

vem

ent D

iffer

ence



Adjusted (Model D)

Asian−White Math Achievement Difference

135

Figure 6.3. Graphical Depictions of Children’s Math Growth from Kindergarten EntryThrough 3rd Grade by Social Class from Table 6.2

15

25

35

45

55

65

75

85

95

Mat

h A

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ent


High SES (+1 sd)

Middle SES

Low SES (−1 sd)


15

25

35

45

55

65

75

85

95

Mat

h A

chie

vem

ent


High SES (+1 sd)

Middle SES

Low SES (−1 sd)


136

Figure 6.4. Graphical Depictions of Social Class Differences in Children’s Math Growthfrom Kindergarten Entry Through 3rd Grade from Table 6.2

−8

−6

−4

−2

0

2

4

6

8

Ach

ieve

men

t Diff

eren

ce






SES Math Achievement Difference

137

Figure 6.5. Graphical Depictions of Children’s Math Growth from Kindergarten EntryThrough 3rd Grade by Concerted Cultivation from Table 6.2

15

25

35

45

55

65

75

85

95

Mat

h A

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Middle C. Cult.


C. Cult.: Unadjusted (Model c)

15

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85

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Mat

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Middle C. Cult.


C. Cult.: Adjusted (Model G)

138

Figure 6.6. Graphical Depictions of Differences in Children’s Math Growth fromKindergarten Entry Through 3rd Grade by Concerted Cultivation from Table 6.2

−6

−4

−2

0

2

4

6

Ach

ieve

men

t Diff

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Low C. Cult.: Unadjusted (Model B)

Low C. Cult. Adjusted (Model E)

High C. Cult.: Unadjusted (Model B)

High C. Cult. Adjusted (Model E)

C. Cult. Math Achievement Difference

139

ment disparities after the first grade in either figure, however. These results are

consistent with previous research documenting that parenting practices are most

important when children are very young (see Farkas and Beron 2003 in the context

of oral language and vocabulary development).


Covariates are often correlated with the random effects in multilevel models, which


the original model set in table 6.2. For example, adding concerted cultivation singly

in model C explains about 50% of the variance in the level-3 intercept, although

much smaller proportions of the variances in the between-child slopes are explained.

That the covariates might be correlated with the random effects is not surprising

when one considers the non-random distribution of children across schools (cite).

For this reason, the next series of models replicate those in table 6.2 but with

the covariates centered around their respective group-means. These models are,

in effect, growth models with school-level fixed effects, which means the between-

student parameters represent average expected math achievement scores between

children who attend the same school. In addition, this approach has the added

benefit of adjusting for constant school-level confounders. Results are presented in

table 6.35 and summarized in figures 6.7 to 6.12.

Initial Status

When children are compared to their peers in the same school, coefficient mag-

nitudes decrease substantially, indicating that the previous estimates were biased

bias by between-school compositional differences. Black and Hispanic children

remain significantly disadvantaged at kindergarten entry and their coefficients de-

crease by 38% and 34%, respectively, when concerted cultivation is added to the

models. This reduction is somewhat smaller than in the non-group-mean centered

models, but still represents significant attenuation in coefficient magnitude. The

Asian advantage when concerted cultivation is added to the model, however, is


140

Table 6.3. Group-Mean Centered Growth Models for Math Achievement Scores (IRT) fromKindergarten Entry Until Spring of Third Grade by Race, Social Class, ‘Concerted Cultivation,’and Selected Covariates (ECLS-K)

Model


Initial Status 18.081 ** 18.074 ** 18.087 ** 18.092 ** 18.081 ** 18.085 ** 18.083 **(0.153) (0.153) (0.153) (0.153) (0.153) (0.153) 0.153

Black −2.468 ** −1.550 ** −1.225 ** −1.034 **(0.312) (0.309) (0.306) 0.313

Hispanic −3.276 ** −2.179 ** −1.794 ** −1.518 **(0.257) (0.259) (0.256) 0.269

Asian 0.339 1.694 ** 1.309 ** 1.623 **(0.367) (0.363) (0.362) 0.384

Other Race −1.239 ** −0.664 −0.516 −0.364(0.381) (0.376) (0.371) 0.370

Social class 2.256 ** 1.776 ** 1.654 ** 1.386 **(0.089) (0.094) (0.095) 0.098

Concerted Cultivation 1.973 ** 1.880 ** 1.392 ** 1.354 ** 1.127 **(0.088) (0.090) (0.092) (0.094) 0.098

Second K. −1.404 ** −1.088 ** −1.261 ** −1.141 ** −0.958 * −0.879 * −0.728 *(0.381) (0.377) (0.382) (0.376) (0.375) (0.371) 0.372

Age at K. Entry 0.484 ** 0.493 ** 0.486 ** 0.482 ** 0.489 ** 0.485 ** 0.483 **(0.017) (0.017) (0.017) (0.017) (0.017) (0.017) 0.016

Kindergarten Slope 1.655 ** 1.655 ** 1.657 ** 1.656 ** 1.656 ** 1.656 ** 1.656 **(0.014) (0.014) (0.014) (0.014) (0.014) (0.014) 0.014

Black −0.281 ** −0.239 ** −0.222 ** −0.218 **(0.039) (0.040) (0.040) 0.041

Hispanic −0.142 ** −0.092 ** −0.072 * −0.060(0.033) (0.033) (0.033) 0.035

Asian −0.030 0.037 0.018 0.040(0.047) (0.048) (0.048) 0.050

Other Race −0.140 ** −0.112 * −0.104 * −0.096 *(0.049) (0.049) (0.049) 0.049

Social class 0.116 ** 0.094 ** 0.087 ** 0.072 **(0.011) (0.012) (0.012) 0.013

Concerted Cultivation 0.095 ** 0.087 ** 0.065 ** 0.060 ** 0.058 **(0.011) (0.012) (0.012) (0.012) 0.013

Second K. −0.163 ** −0.140 ** −0.154 ** −0.152 ** −0.136 ** −0.136 ** −0.134 **(0.047) (0.047) (0.048) (0.048) (0.047) (0.047) 0.047

Changed School −0.098 −0.083 −0.059 −0.053 −0.052 −0.047 −0.033(0.074) (0.074) (0.074) (0.074) (0.074) (0.074) 0.073

Summer Slope 0.533 ** 0.532 ** 0.533 ** 0.534 ** 0.533 ** 0.534 ** 0.534 **(0.055) (0.055) (0.055) (0.055) (0.055) (0.055) 0.055

Black −0.097 −0.040 −0.035 −0.038(0.173) (0.174) (0.175) 0.178

Hispanic 0.033 0.095 0.102 0.088(0.153) (0.155) (0.155) 0.161

Asian 0.529 * 0.625 ** 0.625 ** 0.589 *(0.218) (0.222) (0.222) 0.234

Other Race −0.007 0.038 0.039 0.042(0.210) (0.211) (0.211) 0.212

Social class 0.069 0.039 0.029 0.035(0.054) (0.056) (0.057) 0.058

Concerted Cultivation 0.103 * 0.122 * 0.091 0.113 * 0.115 *(0.052) (0.053) (0.055) (0.056) 0.058

Second K. −0.117 −0.101 −0.102 −0.101 −0.095 −0.097 −0.108(0.219) (0.220) (0.219) (0.219) (0.220) (0.219) 0.220

Changed School 0.304 ** 0.288 * 0.310 ** 0.313 ** 0.296 * 0.300 ** 0.305 **(0.114) (0.114) (0.114) (0.114) (0.114) (0.114) 0.113

1st Grade Slope 2.400 ** 2.400 ** 2.401 ** 2.400 ** 2.401 ** 2.400 ** 2.401 **(0.022) (0.022) (0.022) (0.022) (0.022) (0.022) 0.022

Black −0.248 ** −0.221 ** −0.206 ** −0.189 *(0.063) (0.064) (0.064) 0.065

Hispanic −0.175 ** −0.140 * −0.121 * −0.124 *(0.054) (0.055) (0.055) 0.058

Asian −0.287 ** −0.246 ** −0.265 ** −0.273 **(0.078) (0.079) (0.079) 0.083

Other Race −0.158 * −0.143 −0.133 −0.122(0.077) (0.078) (0.078) 0.078

Social class 0.098 ** 0.082 ** 0.081 ** 0.061 **(0.019) (0.020) (0.020) 0.021

Concerted Cultivation 0.072 ** 0.054 ** 0.045 * 0.028 0.030(0.019) (0.019) (0.020) (0.020) 0.021

Second K. −0.167 * −0.153 * −0.165 * −0.161 * −0.150 m −0.148 m −0.145(0.078) (0.078) (0.078) (0.078) (0.078) (0.078) 0.078

Changed School −0.112 −0.086 −0.104 −0.109 −0.086 −0.092 −0.075(0.085) (0.085) (0.085) (0.085) (0.085) (0.085) 0.084


141

Table 6.3 – Continued: Math Achievement, Group-Mean CenteredModel


2nd − 3rd Grade Slope 1.193 ** 1.193 ** 1.193 ** 1.193 ** 1.193 ** 1.193 ** 1.193 **(0.007) (0.007) (0.007) (0.007) (0.007) (0.007) 0.007

Black −0.112 ** −0.110 ** −0.104 ** −0.096 **(0.020) (0.020) (0.020) 0.020

Hispanic −0.005 −0.003 0.005 −0.002(0.017) (0.017) (0.017) 0.018

Asian 0.076 ** 0.080 ** 0.073 ** 0.068 **(0.023) (0.023) (0.023) 0.025

Other Race 0.017 0.019 0.021 0.025(0.025) (0.025) (0.025) 0.025

Social class 0.031 ** 0.033 ** 0.030 ** 0.023 **(0.006) (0.006) (0.006) 0.006

Concerted Cultivation 0.005 0.005 −0.006 −0.005 −0.004(0.006) (0.006) (0.006) (0.006) 0.006

Second K. −0.106 ** −0.099 ** −0.106 ** −0.105 ** −0.100 ** −0.100 ** −0.098 **(0.024) (0.024) (0.024) (0.024) (0.024) (0.024) 0.024

Changed School −0.014 −0.015 −0.014 −0.013 −0.015 −0.014 −0.015(0.012) (0.012) (0.012) (0.012) (0.012) (0.012) 0.012


1st Grade Slope 0.995 ** 0.995 ** 0.998 ** 0.993 ** 0.994 ** 0.989 ** 0.983 **

2nd − 3rd Grade Slope 0.171 ** 0.172 ** 0.172 ** 0.171 ** 0.172 ** 0.171 ** 0.169 **


1st Grade Slope 0.159 ** 0.159 ** 0.159 ** 0.159 ** 0.159 ** 0.160 ** 0.159 **

2nd − 3rd Grade Slope 0.025 ** 0.025 ** 0.025 ** 0.025 ** 0.025 ** 0.025 ** 0.025 **


even larger when children in the same classrooms are compared. The story is es-

sentially identical for social class, which is reduced by approximately 21% when

concerted cultivation is added to the model.

Although the initial differences are somewhat smaller in the fixed-effects models

(models A through E, in particular), by model G coefficient magnitudes are similar

to those reported in table 6.2, primarily by race, indicating that the non-random

distribution of children across schools is largely accounted for by the covariates

used in the analysis. The association of concerted cultivation with children’s math-

ematics skills at kindergarten entry, while smaller, is still a significant predictor

of children’s school readiness of a modest magnitude, similar in size albeit smaller

than social class. As with the non-centered models, the salience of social class

and concerted cultivation for predicting disparities at kindergarten entry are as

meaningful if not more so than racial categorization.

142

Figure 6.7. Graphical Depictions of Children’s Math Growth from Kindergarten EntryThrough 3rd Grade by Race from Table 6.2, Group-Mean Centered

15

25

35

45

55

65

75

85

95

Mat

h A

chie

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ent


White Black

Hispanic Asian


15

25

35

45

55

65

75

85

95

Mat

h A

chie

vem

ent


White Black

Hispanic Asian


143

Figure 6.8. Graphical Depictions of Race Differences from Whites’ in Children’s MathGrowth from Kindergarten Entry Through 3rd Grade from Table 6.3, Group-Mean Cen-tered

420

−2−4−6−8

−10−12−14

Ach

ieve

men

t Diff

eren

ce



Adjusted (Model D)

Black−White Math Achievement Difference

420

−2−4−6−8

−10−12−14

Ach

ieve

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Adjusted (Model D)

Hispanic−White Math Achievement Difference

420

−2−4−6−8

−10−12−14

Ach

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ce



Adjusted (Model D)

Asian−White Math Achievement Difference

144

Figure 6.9. Graphical Depictions of Children’s Math Growth from Kindergarten EntryThrough 3rd Grade by Social Class from Table 6.3, Group-Mean Centered

15

25

35

45

55

65

75

85

95

Mat

h A

chie

vem

ent


High SES (+1 sd)

Middle SES

Low SES (−1 sd)


15

25

35

45

55

65

75

85

95

Mat

h A

chie

vem

ent


High SES (+1 sd)

Middle SES

Low SES (−1 sd)


145

Figure 6.10. Graphical Depictions of Social Class Differences in Children’s MathGrowth from Kindergarten Entry Through 3rd Grade from Table 6.3, Group-Mean Cen-tered

4

2

0

−2

−4

−6

−8

−10

−12

−14

Ach

ieve

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t Diff

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SES Math Achievement Difference

146

Figure 6.11. Graphical Depictions of Children’s Math Growth from Kindergarten EntryThrough 3rd Grade by Concerted Cultivation from Table 6.3, Group-Mean Centered

15

25

35

45

55

65

75

85

95

Mat

h A

chie

vem

ent



Middle C. Cult.



15

25

35

45

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65

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95

Mat

h A

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Middle C. Cult.



147

Figure 6.12. Graphical Depictions of Differences in Children’s Math Growth fromKindergarten Entry Through 3rd Grade by Concerted Cultivation from Table 6.3, Group-Mean Centered

4

2

0

−2

−4

−6

−8

−10

−12

−14

Ach

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Low C. Cult. Adjusted (Model G)


High C. Cult. Adjusted (Model G)

C. Cult. Math Achievement Difference


Although the results over the kindergarten year are similar in tables 6.3 and 6.2,

important differences surface over the summertime when concerted cultivation

emerges as an important predictor of children’s summer learning. Children from

families one standard deviation above the mean grow at rates approximately .12

points per month higher, a divergence which is readily witnessed in figures 6.11 and

6.12. This finding is consistent with expectations drawn from previous research on

summer learning.

Even when children in the same schools are compared, black and white chil-

dren’s test scores continue to diverge over time as depicted in figures 6.7 and

6.8, with persisting disparities after concerted cultivation is controlled. This is

not surprising, however, because concerted cultivation is not related to children’s

growth over the school year (with the exception of the kindergarten year). In ad-

dition, the evidence strongly suggests that Asian and white children’s math skills

diverge over time in a pattern favoring Asian children. While social class gaps in

growth rates are small, figure 6.10 illustrates that gaps between children in the

148

same schools grow steadily over time, accumulating as children age. Concerted

cultivation’s reduction in the social class gaps is principally through its influence

on the skills children enter school with, with few implications for the growth rates

over the early school years when average within-school associations are estimated

once compositional factors are accounted for.

Summary

In the centered models, concerted cultivation mediates approximately 35% of the

black and Hispanic mathematics disparity, and about 21% of that for social class

at kindergarten entry. Over time, social groups continue to diverge as shown in

the figures. The black-white difference for children in the same schools is expected

to be 10.2 points at the end of third grade, which is reduced to 8.4 points, a 21%

reduction, when concerted cultivation is included in the model, which is smaller

than the 37% reduction found at kindergarten entry. The large difference reflects

the unaccounted differences in children’s growth over the intervening years seen in

figures 6.7 and 6.8, and suggests either within-school processes or unaccounted-for

environmental influences. Although Hispanic and white children also diverge over

time, the differences are far smaller (6.2 versus 4.1 points, a 34% reduction), a

reduction identical to that found at kindergarten entry. Growth in the expected

difference between Hispanic and white children arises primarily over the kinder-

garten and first grade years. In addition, the pattern for Asian children is less

consistent, but implies a divergence over time favoring Asian children if trends

continue, as was found in the non-centered models

By the end of third grade the social class gradient is expected to be 5.2 points,

but is reduced to 4.3 points when concerted cultivation is included in the model.

This reduction is smaller than that for race, being less than 20%. Despite the

importance of concerted cultivation in mathematics development, primarily before

the first grade, strong residual social group disparities remain to be accounted

for and children of different social classes diverge over time, which is shown most

clearly in figure 6.10, although the trend is also readily apparent in figure 6.9.

Children by concerted cultivation diverge through the first grade, including over

the summer at rates larger than those during the school year (see figures 6.11 and

6.12), although disparities stabilize after the first grade.

149





dition to the concerted cultivation coefficients. These models are principally con-

cerned with the (1) the extent to which concerted cultivation mediates race and

class differences, and (2) the average relationship between concerted cultivation

and children’s growth trajectories across social groups. Does concerted cultivation

function equally for groups of differing social status and race/ethnic identification?

Results are reported in table 6.4 for race interactions, table 6.5 for the social class

models, and race by class by concerted cultivation interactions are presented in

table 6.6.


Results for models interaction race and concerted cultivation are reported in ta-

ble 6.4. Model A estimates race and concerted cultivation interactions prior to

controlling for social class, and model B adds social class. Models C and D are

replicates of models A and B, using group-mean centering to eliminate unmeasured

contextual confounders. Consistent with the previous discussion, the coefficients

reported in models C and D are average effects based upon children who attend

the same schools.6






the increments or decrements to the expected difference for the specific group from

whites. For example, the negative coefficient for black children represents a lower

return to concerted cultivation than white children experience. At kindergarten


150

Table 6.4. Race Interactions with Concerted Cultivation Growth Models for Math AchievementScores (IRT) from Kindergarten Entry Until the Spring of Third Grade (ECLS-K)


Variables A B C D

Initial Status 19.073 ** 18.878 ** 18.090 ** 18.084 **(0.139) (0.126) (0.153) (0.153)

Black −2.232 ** −1.602 ** −1.778 ** −1.413 **(0.279) (0.269) (0.326) (0.322)

Hispanic −2.428 ** −1.827 ** −2.133 ** −1.761 **(0.232) (0.226) (0.262) (0.260)

Asian 2.095 ** 1.644 ** 1.927 ** 1.520 **(0.357) (0.351) (0.377) (0.376)

Other −1.454 ** −1.326 ** −0.562 −0.418(0.341) (0.329) (0.379) (0.374)

Social Class 2.059 ** 1.646 **(0.087) (0.095)



(0.246) (0.240) (0.259) (0.255)Hispanic −0.371 −0.409 * −0.368 −0.378

(0.203) (0.198) (0.215) (0.212)Asian 0.263 0.208 0.115 0.075

(0.337) (0.333) (0.342) (0.341)Other −0.206 −0.110 −0.432 −0.406

(0.321) (0.316) (0.332) (0.329)Kindergarten Slope 1.727 ** 1.719 ** 1.656 ** 1.656 **

0.017 0.017 0.014 0.014Black −0.294 ** −0.268 ** −0.258 ** −0.239 **

(0.035) (0.035) (0.041) (0.041)Hispanic −0.113 ** −0.090 ** −0.091 ** −0.071 *

(0.030) (0.030) (0.034) (0.034)Asian 0.023 0.005 0.051 0.030

(0.046) (0.046) (0.049) (0.050)Other −0.098 * −0.088 * −0.114 * −0.106 *

(0.044) (0.044) (0.049) (0.049)Social Class 0.080 ** 0.087 **

(0.011) (0.012)Concerted Cultivation 0.089 ** 0.058 ** 0.088 ** 0.060 **

(0.015) (0.016) (0.016) (0.016)Interactions W/ C. CultivationBlack −0.034 −0.028 −0.040 −0.035

(0.031) (0.031) (0.033) (0.033)Hispanic 0.027 0.027 0.008 0.007

(0.026) (0.026) (0.027) (0.027)Asian 0.034 0.031 0.034 0.033

(0.042) (0.042) (0.042) (0.042)Other 0.004 0.006 0.009 0.011

(0.042) (0.042) (0.043) (0.043)Summer Slope 0.500 ** 0.491 ** 0.534 ** 0.534 **

(0.073) (0.073) (0.055) (0.055)Black 0.000 0.027 −0.047 −0.041

(0.149) (0.150) (0.182) (0.183)Hispanic 0.083 0.110 0.122 0.130

(0.135) (0.136) (0.157) (0.157)Asian 0.563 * 0.552 * 0.572 * 0.571 *

(0.217) (0.217) (0.232) (0.233)Other −0.031 −0.041 0.031 0.032

(0.181) (0.181) (0.212) (0.212)Social Class 0.098 0.030

(0.051) (0.057)Concerted Cultivation 0.101 0.069 0.104 0.095

(0.065) (0.069) (0.071) (0.073)Interactions W/ C. CultivationBlack 0.058 0.060 0.025 0.026

(0.142) (0.142) (0.150) (0.150)Hispanic 0.063 0.065 0.120 0.121

(0.119) (0.119) (0.128) (0.128)Asian −0.133 −0.130 −0.116 −0.119

(0.193) (0.193) (0.199) (0.199)Other −0.004 0.007 −0.049 −0.048

(0.169) (0.169) (0.175) (0.175)


151

Table 6.4 – Continued: Math Achievement, Race Interactions.


Variables A B C D

1st Grade Slope 2.489 ** 2.483 ** 2.400 ** 2.400 **(0.027) (0.028) (0.022) (0.022)

Black −0.302 ** −0.279 ** −0.216 ** −0.199 **(0.056) (0.056) (0.067) (0.067)

Hispanic −0.150 ** −0.130 ** −0.144 * −0.126 *(0.049) (0.049) (0.056) (0.056)

Asian −0.277 ** −0.296 ** −0.241 ** −0.261 **(0.077) (0.077) (0.083) (0.083)

Other −0.209 ** −0.196 ** −0.129 −0.120(0.068) (0.068) (0.078) (0.078)

Social Class 0.066 ** 0.080 **(0.019) (0.020)

Concerted Cultivation 0.098 ** 0.070 ** 0.082 ** 0.056 *(0.024) (0.025) (0.026) (0.027)

Interactions W/ C. CultivationBlack −0.060 −0.053 −0.050 −0.044

(0.052) (0.052) (0.055) (0.055)Hispanic −0.063 −0.064 −0.078 −0.079

(0.042) (0.042) (0.045) (0.045)Asian −0.021 −0.024 −0.053 −0.053

(0.068) (0.068) (0.070) (0.070)Other −0.002 −0.002 −0.009 −0.008

(0.064) (0.064) (0.066) (0.066)2nd-3rd Grade Slope 1.215 ** 1.212 ** 1.193 ** 1.193 **

(0.009) (0.009) (0.007) (0.007)Black −0.118 ** −0.107 ** −0.113 ** −0.106 **

(0.018) (0.018) (0.021) (0.021)Hispanic −0.001 0.010 0.003 0.010

(0.015) (0.015) (0.017) (0.017)Asian 0.079 ** 0.071 ** 0.074 ** 0.067 *

(0.022) (0.022) (0.024) (0.024)Other −0.008 −0.005 0.016 0.018

(0.022) (0.022) (0.025) (0.025)Social Class 0.035 ** 0.030 **

(0.006) (0.006)Concerted Cultivation −0.002 −0.015 m −0.003 −0.013


(0.016) (0.016) (0.017) (0.017)Hispanic 0.032 * 0.031 * 0.031 * 0.031 *

(0.013) (0.013) (0.014) (0.014)Asian 0.005 0.004 0.005 0.004

(0.020) (0.020) (0.021) (0.021)Other −0.002 0.001 −0.016 −0.014

(0.021) (0.021) (0.022) (0.022)


152

entry, black children gain only (2.1-1.0=) 1.1 points for each standard deviation

increase in concerted cultivation, compared to all other children who receive 2.1

points (model C), on average. This finding is contrary to expectations taken from

Lareau (2003), who found that concerted cultivation operated similarly for white

and black children, although Sun (1998) found that black children receive lower

returns to parental investments on math and science tests.7 This finding is similar

to that found previously for general knowledge, and it will be given more attention

in section 6.2.3. There are no other significant interactions in the model.


In the previous section the question of whether or not concerted cultivation relates

to children’s general knowledge achievement differently by race/ethnic group. This

section is conceptually similar, although the question is posed for social class in-

stead. Although social class is used as a standardized, continuous covariate in

the preceding analyses, in the following section, social class is coded as a series of

dummy variables. By coding social class as a series of categorical indicators, the

interpretation of the interactions is similar to those in the race interaction models.

The first grouping, low social class, captures the bottom quartile of the distribu-

tion, average social class, the reference category, captures the middle 50% of the

distribution, and high social class is composed of the top quartile. The results

presented in table 6.5 follow a parallel progression to those presented in table 6.4.

There is little evidence of concerted cultivation interactions by social class. Al-

though children from lower social class families receive fewer returns to concerted

cultivation in the non-group-mean centered models (models A and B), the relation-

ship reduces to non-significance when children in the same schools are compared.

Surprisingly, however, there is evidence that higher social class children receive

fewer returns to concerted cultivation than middle-class children during the sec-

ond and third grade years.

7Farkas and Beron (2004) report similar findings by social class and the HOME inventorieswith the PPVT for social class, mother’s verbal AFQT, and cognitive stimulation (pg. 484, Table4).

153

Table 6.5. Social Class Interactions with Concerted Cultivation Growth Models for MathAchievement Scores (IRT) from Kindergarten Entry Until the Spring of Third Grade (ECLS-K)


Variables A B C D

Initial Status 18.204 ** 18.701 ** 18.099 ** 18.102 **(0.124) (0.141) (0.153) (0.153)

Black −1.445 ** −1.288 **(0.250) (0.306)

Hispanic −1.956 ** −1.862 **(0.221) (0.257)

Asian 1.539 ** 1.436 **(0.335) (0.362)

Other −1.412 ** −0.507(0.330) (0.372)

Low SES (< −25%) −2.459 ** −2.220 ** −1.944 ** −1.788 **(0.225) (0.224) (0.234) (0.233)

High SES (> +25%) 3.203 ** 2.954 ** 2.462 ** 2.276 **(0.210) (0.210) (0.219) (0.219)


Interactions W/ C. CultivationLow SES (< −25%) −0.436 * −0.497 * −0.246 −0.294

(0.202) (0.201) (0.209) (0.208)High SES (> +25%) −0.039 0.082 0.032 0.102


(0.016) (0.019) (0.014) (0.014)Black −0.253 ** −0.228 **

(0.033) (0.040)Hispanic −0.095 ** −0.074 *

(0.029) (0.033)Asian −0.004 0.021

(0.044) (0.048)Other −0.089 * −0.106 *

(0.043) (0.049)Low SES (< −25%) −0.109 ** −0.089 ** −0.090 ** −0.077 *

(0.029) (0.029) (0.030) (0.030)High SES (> +25%) 0.123 ** 0.110 ** 0.133 ** 0.124 **

(0.027) (0.028) (0.029) (0.029)Concerted Cultivation 0.077 ** 0.061 ** 0.072 ** 0.064 **

(0.016) (0.016) (0.016) (0.017)Interactions W/ C. CultivationLow SES (< −25%) 0.019 0.026 0.008 0.013

(0.026) (0.026) (0.027) (0.027)High SES (> +25%) −0.017 −0.011 −0.017 −0.014

(0.027) (0.027) (0.028) (0.028)Summer Slope 0.463 ** 0.426 ** 0.534 ** 0.534 **

(0.069) (0.080) (0.055) (0.055)Black 0.002 −0.039

(0.142) (0.175)Hispanic 0.074 0.101

(0.131) (0.155)Asian 0.611 ** 0.621 **

(0.205) (0.222)Other −0.031 0.034

(0.179) (0.211)Low SES (< −25%) 0.059 0.066 0.080 0.088

(0.129) (0.130) (0.135) (0.135)High SES (> +25%) 0.298 * 0.270 * 0.187 0.164

(0.123) (0.124) (0.131) (0.131)Concerted Cultivation 0.088 0.108 0.095 0.113


(0.118) (0.118) (0.122) (0.122)High SES (> +25%D) −0.097 −0.076 −0.102 −0.084

(0.123) (0.123) (0.128) (0.128)


154

Table 6.5 – Continued: Math Achievement, SES Interactions


Variables A B C D

1st Grade Slope 2.429 ** 2.510 ** 2.402 ** 2.401 **(0.026) (0.030) (0.022) (0.022)

Black −0.274 ** −0.206 **(0.053) (0.064)

Hispanic −0.112 * −0.118 *(0.047) (0.055)

Asian −0.303 ** −0.261 **(0.073) (0.079)

Other −0.210 ** −0.132(0.068) (0.078)

Low SES (< −1 SD) −0.163 ** −0.145 ** −0.166 ** −0.156 **(0.047) (0.047) (0.049) (0.049)

High SES (> +25%) 0.070 0.072 0.078 0.080(0.045) (0.046) (0.048) (0.048)

Concerted Cultivation 0.086 ** 0.057 * 0.058 * 0.041(0.025) (0.026) (0.027) (0.027)


(0.042) (0.042) (0.043) (0.043)High SES (> +25%) −0.029 −0.031 −0.022 −0.027

(0.045) (0.045) (0.047) (0.047)2nd-3rd Grade Slope 1.205 ** 1.213 ** 1.193 ** 1.193 **

(0.008) (0.009) (0.007) (0.007)Black −0.109 ** −0.105 **

(0.017) (0.020)Hispanic 0.009 0.008

(0.015) (0.017)Asian 0.078 ** 0.071 **

(0.021) (0.023)Other −0.001 0.021

(0.022) (0.025)Low SES (< −25%) −0.051 ** −0.045 ** −0.045 ** −0.042 **

(0.014) (0.014) (0.015) (0.015)High SES (> +25%) 0.070 ** 0.062 ** 0.063 ** 0.058 **

(0.014) (0.014) (0.014) (0.014)Concerted Cultivation −0.002 −0.003 −0.001 −0.001


(0.013) (0.013) (0.013) (0.013)High SES (> +25%) −0.040 ** −0.036 ** −0.043 ** −0.040 **

(0.013) (0.013) (0.014) (0.014)


155




distribution (‘A’ models), the middle-50% (‘B’ models), and the top quartile (‘C’




perplexing too (e.g. Sun 1998; Farkas and Beron 2004). Results from table 6.6

illustrate which black children are benefitting, and which are not.

Lower social class black children, as with the general knowledge scores, do not

receive the same benefit from concerted cultivation as white children, and corre-

spondingly do not receive the same detriments. Because most of the lower-class

black children come from homes with negative values on the concerted cultivation

factor, the negative interaction implies that net of the black-white main effect,

black children are doing better than expected. Although the interaction terms

for middle- and upper-class black children are non-significant, they are negative

in value across specifications. Lower class Hispanic children follow a similar pat-

tern for the math test, although the non-significant interaction terms for the other

social classes tend to be positive.

6.3 Summary

In the foregoing analysis, concerted cultivation was shown to be an important pre-

dictor of children’s mathematics achievement at kindergarten entry. Although the

coefficient magnitude decreased as covariates were added to the model, concerted

cultivation persisted as an important predictor of children’s school readiness—of

similar magnitude to social class, in fact, even when children in the same schools

were compared. Given the high correlation between social class and concerted

cultivation, the social class effects, though large, are significantly larger via indi-

rect effects through concerted cultivation. In addition, concerted cultivation was

shown to be related to children’s mathematics growth over the summertime, al-

though inclusion of concerted cultivation did little to ameliorate the much larger

156

Table 6.6. Race & Class Interactions with Concerted Cultivation Growth Models for Math-ematics Achievement Scores (IRT) from Kindergarten Entry Until the Spring of Third Grade(ECLS-K)

A-1 A-2 B-1 B-2 C-1 C-2

Initial Status 16.182 ** 16.352 ** 18.669 ** 18.259 ** 21.890 ** 17.915 **(0.240) (0.466) (0.148) (0.360) (0.321) (0.867)

Black −1.835 ** −1.625 ** −1.518 ** −1.167 ** −2.291 ** −1.148(0.437) (0.451) (0.327) (0.333) (0.832) (0.848)

Hispanic −2.283 ** −1.967 ** −1.776 ** −1.430 ** −2.956 ** −2.243 **(0.401) (0.451) (0.283) (0.299) (0.813) (0.821)

Asian 2.188 * 2.560 * 1.028 * 1.090 * 2.731 ** 3.123 **(1.015) (1.076) (0.510) (0.541) (0.721) (0.808)

Other −2.004 ** −1.663 ** −1.956 ** −1.589 ** 0.031 0.221(0.630) (0.625) (0.414) (0.406) (1.070) (1.051)

Social Class 0.465 * 1.899 ** 1.729 **(0.191) (0.262) (0.323)


Interactions W/ C. CultivationBlack −1.374 ** −1.235 ** −0.659 −0.623 −0.361 −0.546

(0.385) (0.385) (0.356) (0.352) (0.876) (0.862)Hispanic −1.061 ** −1.002 ** 0.269 0.119 0.695 0.415

(0.345) (0.360) (0.312) (0.319) (0.876) (0.867)Asian −0.625 −0.396 0.607 0.446 0.020 0.207

(0.749) (0.735) (0.550) (0.543) (0.719) (0.721)Other −0.387 −0.303 0.029 −0.015 −0.418 −0.431

(0.609) (0.598) (0.440) (0.433) (0.978) (0.962)


Black −0.288 ** −0.299 ** −0.267 ** −0.250 ** −0.461 ** −0.419 **(0.064) (0.066) (0.044) (0.046) (0.101) (0.105)

Hispanic −0.233 ** −0.239 ** −0.045 −0.024 −0.114 −0.080(0.060) (0.068) (0.038) (0.041) (0.097) (0.099)

Asian −0.024 −0.016 0.031 0.061 −0.103 −0.080(0.156) (0.159) (0.072) (0.075) (0.091) (0.099)

Other −0.152 −0.158 −0.057 −0.039 −0.159 −0.135(0.094) (0.095) (0.057) (0.057) (0.127) (0.127)

Social Class 0.004 0.073 * 0.096 **(0.029) (0.036) (0.038)

Concerted Cultivation 0.165 ** 0.149 ** 0.065 ** 0.052 * 0.001 0.020(0.040) (0.040) (0.021) (0.022) (0.033) (0.035)

Interactions W/ C. CultivationBlack −0.105 −0.101 −0.099 * −0.100 * 0.194 m 0.183

(0.058) (0.057) (0.048) (0.048) (0.103) (0.103)Hispanic −0.109 * −0.102 * 0.010 −0.002 0.040 0.030

(0.051) (0.052) (0.043) (0.044) (0.104) (0.104)Asian −0.125 −0.119 0.117 0.115 0.135 0.133

(0.107) (0.107) (0.079) (0.079) (0.088) (0.088)Other −0.138 −0.136 0.045 0.044 0.044 0.026

(0.089) (0.089) (0.060) (0.060) (0.116) (0.116)

‘m’ p < .06, ‘*’p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.Note 1: Table continued on the following page.Note 2: ‘A’ models are for the lower quantile of the social class distribution, ‘B’ models are estimated from themiddle 50% of the distribution, and ‘C’ models use the top quantile of the social class distribution. For theconcerted cultivation distribution by race and social class, see figure 4Note 3: ‘-1’ models include second time kindergartner, age at kindergarten entry (-66 months), and whether ornot the child changed schools over the given period. ‘-2’ models include the full covariate list.

157

Table 6.6 – Continued: Mathematics Achievement, Race & Social Class Interactions

A-1 A-2 B-1 B-2 C-1 C-2

Summer Slope 0.501 ** 0.623 ** 0.486 ** 0.343 * 0.570 ** 0.268(0.155) (0.230) (0.089) (0.121) (0.171) (0.302)

Black −0.102 −0.145 −0.115 −0.128 0.879 * 0.977 *(0.284) (0.287) (0.190) (0.196) (0.443) (0.451)

Hispanic 0.121 0.146 −0.024 −0.097 0.024 0.070(0.277) (0.298) (0.178) (0.188) (0.408) (0.423)

Asian 0.985 1.139 0.608 0.514 0.790 m 0.717(0.760) (0.806) (0.329) (0.344) (0.406) (0.447)

Other 0.242 0.221 −0.242 −0.272 −0.025 0.019(0.331) (0.335) (0.236) (0.238) (0.578) (0.581)

Social Class 0.032 −0.051 0.161(0.131) (0.159) (0.176)

Concerted Cultivation 0.249 0.229 −0.016 −0.016 0.132 0.061(0.166) (0.169) (0.093) (0.097) (0.150) (0.157)

Interactions W/ C. CultivationBlack −0.275 −0.272 0.464 * 0.514 * −0.784 −0.787

(0.258) (0.256) (0.213) (0.213) (0.466) (0.468)Hispanic −0.110 −0.096 0.181 0.286 0.365 0.363

(0.235) (0.236) (0.196) (0.200) (0.445) (0.452)Asian −0.080 0.012 0.208 0.312 −0.461 −0.432

(0.488) (0.498) (0.353) (0.354) (0.401) (0.408)Other −0.159 −0.131 0.128 0.159 0.075 0.009

(0.310) (0.316) (0.241) (0.240) (0.549) (0.550)1st Grade Slope 2.336 ** 2.440 ** 2.502 ** 2.631 ** 2.636 ** 2.707 **

(0.060) (0.086) (0.034) (0.045) (0.063) (0.109)Black −0.225 * −0.240 * −0.258 ** −0.238 ** −0.717 ** −0.657 **

(0.107) (0.111) (0.072) (0.073) (0.159) (0.163)Hispanic −0.096 −0.156 −0.110 −0.086 −0.019 −0.011

(0.098) (0.109) (0.065) (0.068) (0.143) (0.149)Asian −0.258 −0.367 −0.299 * −0.307 ** −0.390 ** −0.377 *

(0.265) (0.282) (0.120) (0.125) (0.141) (0.158)Other −0.286 * −0.275 * −0.180 * −0.162 −0.342 −0.314

(0.136) (0.137) (0.090) (0.091) (0.208) (0.209)Social Class 0.010 0.177 ** −0.053

(0.046) (0.060) (0.062)Concerted Cultivation 0.119 m 0.098 0.082 * 0.063 0.007 0.034

(0.063) (0.063) (0.035) (0.037) (0.054) (0.057)Interactions W/ C. CultivationBlack −0.048 −0.049 −0.152 m −0.161 * 0.248 0.237

(0.098) (0.097) (0.079) (0.079) (0.164) (0.164)Hispanic −0.113 −0.090 −0.036 −0.052 −0.308 * −0.317 *

(0.082) (0.083) (0.071) (0.072) (0.154) (0.157)Asian −0.063 −0.075 0.004 −0.008 0.017 0.032

(0.174) (0.177) (0.122) (0.123) (0.140) (0.144)Other −0.070 −0.060 −0.027 −0.035 0.140 0.130

(0.131) (0.133) (0.093) (0.092) (0.199) (0.199)2nd − 3rd Grade Slope 1.148 ** 1.231 ** 1.215 ** 1.253 ** 1.274 ** 1.271 **

(0.021) (0.030) (0.011) (0.015) (0.017) (0.030)Black −0.111 ** −0.098 * −0.108 ** 0.101 ** −0.025 −0.010

(0.036) (0.037) (0.023) (0.023) (0.044) (0.046)Hispanic 0.079 * 0.069 0.008 0.008 −0.047 −0.037

(0.034) (0.038) (0.020) (0.021) (0.042) (0.043)Asian 0.185 * 0.167 * 0.051 0.019 0.058 0.079 m

(0.074) (0.078) (0.035) (0.037) (0.036) (0.042)Other 0.013 0.012 −0.031 0.025 0.020 0.024

(0.053) (0.055) (0.029) (0.029) (0.057) (0.057)Social Class 0.026 0.012 0.014

(0.015) (0.018) (0.017)Concerted Cultivation −0.008 −0.014 −0.002 0.001 −0.043 ** −0.043 **

(0.022) (0.022) (0.011) (0.011) (0.015) (0.015)Interactions W/ C. CultivationBlack 0.027 0.028 0.012 0.005 −0.065 −0.080

(0.032) (0.032) (0.025) (0.025) (0.046) (0.046)Hispanic 0.067 * 0.068 * −0.014 0.002 0.061 0.054

(0.029) (0.029) (0.022) (0.023) (0.046) (0.047)Asian 0.047 0.039 −0.011 0.005 0.018 0.016

(0.054) (0.056) (0.033) (0.034) (0.038) (0.038)Other 0.021 0.010 −0.023 −0.029 0.006 0.002

(0.054) (0.058) (0.031) (0.030) (0.052) (0.052)

‘m’ p < .06, ‘*’p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.Note 1: ‘A’ models are for the lower quantile of the social class distribution, ‘B’ models are estimated from themiddle 50% of the distribution, and ‘C’ models use the top quantile of the social class distribution. For theconcerted cultivation distribution by race and social class, see figure 4Note 2: ‘-1’ models include second time kindergartner, age at kindergarten entry (-66 months), and whether ornot the child changed schools over the given period. ‘-2’ models include the full covariate list.

158

and significant Asian summer advantage. Given that Asian families are much less

likely to practice concerted cultivation than white families, the inability of this

measure to mediate the Asian summer advantage is not surprising.

Concerted cultivation was also demonstrated to be an important mediator of

race and class differences at kindergarten entry, although residual race and social

class effects remain to be explained. When comparing children in the general pop-

ulation, concerted cultivation explained approximately 40% of black and Hispanic

deficits at kindergarten entry, although this number dropped slightly when the

non-random distribution of children across schools and stable school characteris-

tics were accounted for. The proportion of the social class effect accounted for by

concerted cultivation was smaller, at 21%. Proportions of explained variance on

the temporal slopes, however, were less meaningful and often small in magnitude.

Indeed, other than the differential summer growth by concerted cultivation, con-

certed cultivation was not systematically associated with children’s growth during

the school year.

In fact, the impact of concerted cultivation appears to get smaller as time

goes on for mathematics, which may be a product of the increasing abstraction

of mathematics as children age, although children at these young ages are still

engaging in simple counting and addition tasks which could presumably benefit

from concerted cultivation. Unfortunately, black children continue to fall behind,

and by the end of third grade only about 21% of the black-white difference is

accounted for by concerted cultivation. Social class differences also continue to

increase with time.

As with the general knowledge models, concerted cultivation was found to func-

tion differently for black children at kindergarten entry. There was a similar finding

for Hispanic children, although the Hispanic-concerted cultivation interaction was

not significant for the general knowledge test. These effects, which mean that

concerted cultivation has a smaller effect for black and Hispanic children, indicate

that children from a disadvantaged part of the concerted cultivation distribution

are not doing as poorly as expected, although it must be noted that the black and

Hispanic main-effects are substantial.

CHAPTER

SEVEN

Reading Achievement

Reading skills, perhaps more than any other academic competency, encompass a

base of proficiencies which are implicated uniformly throughout the lives of people

living in the Western world. The ability to read intimately connects academic

subjects together, including mathematics and the sciences, and is correspondingly

tied to individuals’ economic potentialities. More generally, in democratic societies

where there is a certain expectation (or hope, at least) that individuals will be in-

formation consumers, literacy is a gateway to information and as such, a major

means through which the populace is expected to monitor their elected represen-

tatives. Indeed, reading skills are implicated from such base activities as ordering

a sandwich for lunch to a physicist’s ability to discuss quantum Hamiltonians with

peers.

At the most mundane, anybody who has ever traveled in a foreign country

while not being knowledgeable of the native language knows it is possible to get

by with very few literacy skills. Millions of immigrants further demonstrate this

fact every day in the U.S. However, the envelope of intellectual knowledge and

labor market success are severely limited for those people because reading skills

have become so implicit in the structuring of modern society and because, as a

result, reading skills must be highly developed in many circumstances. Although

most U.S. children learn to read, there are large, systematic inequalities in chil-

dren’s reading competencies by social group (Farkas and Beron 2004; Phillips et

al. 1998a,b; Lee and Burkam 2002). What role does concerted cultivation play

160

in the development of children’s reading skills, and what are the implications for

social group disparities?

7.1 Reading Achievement Growth

Results for two descriptive, baseline reading achievement growth models are pre-

sented in table 7.1 for baseline comparisons with later models. The growth parame-

ters are arrayed across the columns, rather than down the rows as is typical. The

first model includes no control variables, while the second model includes covariates

for age at kindergarten entry, whether the child is a second time kindergartner, and

whether the child changed schools over a given period—covariates which are in-

cluded in all subsequent models. Children begin school with broadly varying math

skills, with a mean of 23.2 points and a standard deviation of (√

71.5 =) 8.5 points.

Unlike the math test, children’s reading skills, surprisingly, do not increase over the

summertime—they decline.1 This is surprising since mathematics skills are typi-

cally considered less germane to development in out-of-school contexts. As with

the math test, however, there is significant heterogeneity in summertime learning

rates, particularly when considered against the general knowledge test. With a

between-child standard deviation (√

3.41 =) 1.8, some children learn at nearly the

same rates as the average kindergarten growth rate (1.9 points per month), while

other children lose very significant ground.

Children acquire reading skills most quickly during the first grade, but the

slope parameter also has the greatest school-time variance over this period (1.9 or

[√

1.9 =] 1.4 standard deviations).2 As with mathematics, both mean differences

in the growth parameters and differences in parameter variability were key reasons

that led Downey and colleagues (2004) to conclude that schools have an equalizing

influence on children’s academic competencies.

The correlations between growth parameters indicate that children who begin

school with more reading skills do not learn appreciable more over the kindergarten

1Cooper et al. 1996 found that summer was more detrimental for math than reading.2The second and third grade slope is not directly comparable to the kindergarten and first

grade slopes because it is a mixture of two summer slopes and two school year slopes. Never-theless, if the summer growth was 0 over this time period, and growth was equivalent over thesecond and third grade school years, the expected during-school growth rate would be 2.4 pointsper month.

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Table 7.1. Null Growth Models for Reading Achievement Scores (IRT) from Kinder-garten Entry Until Spring of Third Grade

Intercept Kindergarten Summer 1st 2nd − 3rd

Null Model (w/out Covariates)Growth Parameters 23.220 1.860 −0.181 3.361 1.576

(0.162) (0.018) (0.056) (0.029) (0.009)

L-2 Random EffectsIntercept 71.494Kindergarten 0.052 0.876Summer 0.227 0.090 3.3941st 0.040 0.053 −0.219 1.8522nd − 3rd −0.250 −0.244 −0.214 −0.335 0.354

L-3 Random EffectsIntercept 17.767Kindergarten 0.298 0.193Summer 0.353 −0.201 0.4931st 0.411 0.188 −0.241 0.4342nd − 3rd −0.002 −0.478 0.041 −0.178 0.043

Null Model (w/ Covariates)Growth Parameters 23.412 1.877 −0.151 3.385 1.586

(0.162) (0.018) (0.058) (0.029) (0.009)Second Time K. 0.970 −0.343 −0.226 −0.528 −0.052

(0.441) (0.056) (0.225) (0.088) (0.032)Age at K. Entry 0.304

(0.020)Changed Schools −0.210 −0.118 −0.423 −0.044

(0.091) (0.118) (0.115) (0.015)

L-2 Random EffectsIntercept 69.622Kindergarten 0.056 0.871Summer 0.226 0.090 3.3931st 0.047 0.047 −0.220 1.8392nd − 3rd −0.245 −0.246 −0.215 −0.337 0.353

L-3 Random EffectsIntercept 17.169Kindergarten 0.277 0.191Summer 0.358 −0.212 0.5021st 0.392 0.181 −0.257 0.4282nd − 3rd 0.000 −0.476 0.037 −0.184 0.043


162

and first grade years than their schoolmates, although higher skilled children learn

less over the second and third grade and more over the summers than children in

the same schools. Surprisingly, children who had higher than average growth rates

over the kindergarten and first grade years tended to grow slower over the course

of the second and third grade.

Average growth rates were higher in schools with higher average achievement

at kindergarten entry. Children in schools with higher initial achievement tended

to be in schools with higher than average growth rates during kindergarten, the

summer, and first grade, while average second and third grade growth rates were

unrelated to initial status. Higher than average summer growth was associated

with slower average kindergarten and first grade growth, however. The average

kindergarten growth rate was strongly negatively associated with second and third

grade average growth (ρ = −.48). Although the between-student correlations

are not reflective of a Matthew effect within school, the positive between-school

correlations between initial status and the slopes prior to the second and third

grade years suggests that schools with higher average levels of initial achievement

have higher than average growth rates.

The results of the initial, baseline growth model highlight that:

1. There is substantial between-child variability both in children’s reading skillswhen they enter school and in the temporal patterns of change.

2. Children learn more reading skills within than outside of school, with chil-dren, on average, loosing ground over the summer. Even though averagegrowth is negative, however, many children accrue significant advantagesover the summer.

3. Children’s initial skills are not strongly related to growth during the kinder-garten and first grade years in the between-student model, although initialskills are negatively related to growth over the second and third grade. Higherskill levels at kindergarten entry are positively correlated with the summerlearning slope.

4. The average growth rate in schools with higher average levels of achievementat kindergarten entry tend to be higher, while the higher average growthrates over the kindergarten and first grade years are associated with loweraverage growth rates during the second and third grade.

163


Parameter estimates for descriptive and explanatory three-level growth models are

presented in table 7.2. In addition, coefficient reduction summaries and standard-

ized coefficients are available in Appendix tables A.9 and A.10. All coefficients

for equations A through F are reported in the table while the full model results

for model G are available in Appendix table A.12 (along with a series of interme-

diate models).3 Models A through C are descriptive in nature, estimating what

is essentially the “bivariate” relationship between race (A), social class (B), and

concerted cultivation (C) on children’s general knowledge trajectories. The next

model set in the series, D and E, adjusts the race (D) and social class differences

(E) for differences in concerted cultivation levels. Race, social class, and concerted

cultivation are included simultaneously in model F, while the full control list is

entered into the equation in model G.4 All models adjust for whether or not the

child is a second time kindergartner, age at kindergarten entry, and whether or

not the child moved during any given period, per the second null model presented

in table 7.1.

Initial Status

From columns A and B of table 7.2, it is clear that social group status is signifi-

cantly related to children’s early reading skills. The difference between black and

white students, 2 points or .24 standard deviations, is smaller than for math (the

difference is .4 standard deviations for math). Hispanic children face an even larger

disadvantage at kindergarten entry, -3.5 points or .41 standard deviations, which is

also smaller than for math (which is .55 standard deviations), while Asian children

have a 1 point or .11 standard deviation advantage. The social class disadvantage

is larger than those across race groups for the reading test, 3.2 points per standard

deviation of social class, which is an effect size of .37. Children differing by two

standard deviations are expected to begin school with a reading score difference of

.76 standard deviations or 6.5 points.

The measure of concerted cultivation is associated with children’s reading skills


4Results and full covariate list for model G are displayed in Appendix table A.12.

164

Table 7.2. Growth Models for Reading Achievement Scores (IRT) from Kindergarten EntryUntil Spring of Third Grade by Race, Social Class, Concerted Cultivation, and Selected Covari-ates (ECLS-K)

Model


Initial Status 24.299 ** 23.348 ** 23.323 ** 23.546 ** 23.281 ** 23.343 ** 21.686 **(0.173) (0.123) (0.133) (0.154) (0.116) (0.140) (0.319)

Black −2.025 ** −0.412 0.221 0.350(0.307) (0.302) (0.292) (0.301)

Hispanic −3.541 ** −1.997 ** −1.406 ** −1.131 **(0.274) (0.272) (0.265) (0.280)

Asian 0.994 * 3.083 ** 2.559 ** 2.929 **(0.403) (0.393) (0.387) (0.425)

Other Race −1.492 ** −0.802 * −0.738 m −0.509(0.403) (0.392) (0.380) (0.376)

Social class 3.152 ** 2.450 ** 2.337 ** 1.972 **(0.093) (0.101) (0.102) (0.108)


Second K. 1.193 ** 1.563 ** 1.493 ** 1.632 ** 1.812 ** 1.900 ** 2.098 **(0.442) (0.431) (0.434) (0.432) (0.427) (0.426) (0.422)

Age at K. Entry 0.295 ** 0.319 ** 0.295 ** 0.291 ** 0.308 ** 0.304 ** 0.325 **(0.020) (0.019) (0.019) (0.019) (0.019) (0.019) (0.018)


Black −0.258 ** −0.209 ** −0.181 ** −0.160 **(0.038) (0.039) (0.039) (0.040)

Hispanic −0.118 ** −0.051 −0.021 −0.023(0.034) (0.035) (0.035) (0.037)

Asian 0.119 * 0.194 ** 0.171 ** 0.166 **(0.049) (0.051) (0.051) (0.055)

Other Race −0.052 −0.023 −0.010 −0.001(0.050) (0.051) (0.051) (0.051)

Social class 0.137 ** 0.116 ** 0.103 ** 0.087 **(0.012) (0.013) (0.013) (0.014)

Concerted Cultivation 0.096 ** 0.089 ** 0.053 ** 0.051 ** 0.025(0.012) (0.013) (0.013) (0.014) (0.014)

Second K. −0.333 ** −0.309 ** −0.329 ** −0.323 ** −0.307 ** −0.306 ** −0.279 **(0.055) (0.056) (0.057) (0.056) (0.056) (0.055) (0.056)

Changed School −0.202 * −0.193 * −0.159 −0.153 −0.163 −0.155 −0.132(0.091) (0.090) (0.091) (0.091) (0.090) (0.090) (0.089)

Summer Slope −0.198 ** −0.150 ** −0.151 ** −0.242 ** −0.151 * −0.255 ** −0.338 **(0.069) (0.058) (0.058) (0.071) (0.058) (0.071) (0.099)

Black 0.048 0.147 0.201 0.212(0.138) (0.142) (0.143) (0.147)

Hispanic 0.112 0.205 0.251 0.268(0.134) (0.138) (0.138) (0.148)

Asian 0.685 ** 0.818 ** 0.772 ** 0.762 **(0.206) (0.210) (0.210) (0.226)

Other Race 0.020 0.057 0.059 0.048(0.178) (0.180) (0.180) (0.181)

Social class 0.227 ** 0.212 ** 0.208 ** 0.209 **(0.047) (0.052) (0.053) (0.055)

Concerted Cultivation 0.107 * 0.155 ** 0.026 0.076 0.043(0.046) (0.049) (0.052) (0.054) (0.056)

Second K. −0.239 −0.179 −0.213 −0.218 −0.181 −0.189 −0.164(0.225) (0.225) (0.225) (0.225) (0.225) (0.225) (0.226)

Changed School −0.100 −0.159 −0.099 −0.084 −0.144 −0.128 −0.103(0.118) (0.117) (0.118) (0.117) (0.117) (0.116) (0.116)

1st Grade Slope 3.527 ** 3.383 ** 3.385 ** 3.483 ** 3.383 ** 3.470 ** 3.520 **(0.033) (0.028) (0.028) (0.033) (0.028) (0.033) (0.042)

Black −0.423 ** −0.332 ** −0.294 ** −0.289 **(0.061) (0.062) (0.062) (0.064)

Hispanic −0.379 ** −0.270 ** −0.230 ** −0.231 **(0.056) (0.057) (0.057) (0.061)

Asian −0.193 * −0.060 −0.091 −0.082(0.083) (0.085) (0.085) (0.091)

Other Race −0.160 * −0.119 −0.108 −0.094(0.080) (0.080) (0.080) (0.080)

Social class 0.208 ** 0.158 ** 0.145 ** 0.115 **(0.020) (0.022) (0.022) (0.023)


Second K. −0.506 ** −0.482 ** −0.498 ** −0.484 ** −0.471 ** −0.462 ** −0.414 **(0.088) (0.089) (0.089) (0.088) (0.089) (0.088) (0.088)

Changed School −0.420 ** −0.369 ** −0.423 ** −0.415 ** −0.376 ** −0.372 ** −0.321 **(0.115) (0.114) (0.114) (0.114) (0.113) (0.113) (0.112)


165

Table 7.2 – Continued: Reading AchievementModel


2nd − 3rd Grade Slope 1.636 ** 1.586 ** 1.584 ** 1.631 ** 1.585 ** 1.632 ** 1.637 **(0.011) (0.009) (0.009) (0.011) (0.009) (0.011) (0.015)

Black −0.156 ** −0.148 ** −0.146 ** −0.153 **(0.021) (0.022) (0.022) (0.023)

Hispanic −0.048 * −0.038 * −0.037 m −0.047 *(0.019) (0.019) (0.019) (0.021)

Asian −0.238 ** −0.230 ** −0.229 ** −0.236 **(0.027) (0.028) (0.028) (0.031)

Other Race −0.130 ** −0.129 ** −0.130 ** −0.129 **(0.029) (0.029) (0.029) (0.029)

Social class 0.008 −0.003 −0.002 −0.014(0.007) (0.008) (0.008) (0.008)

Concerted Cultivation 0.027 ** 0.008 0.027 ** 0.008 −0.001(0.007) (0.007) (0.008) (0.008) (0.008)

Second K. −0.047 −0.050 −0.046 −0.045 −0.047 −0.046 −0.040(0.032) (0.033) (0.032) (0.032) (0.032) (0.032) (0.033)

Changed School −0.031 * −0.044 ** −0.035 * −0.026 −0.036 * −0.028 m −0.021(0.015) (0.015) (0.015) (0.014) (0.015) (0.014) (0.014)


1st Grade Slope 1.836 ** 1.824 ** 1.827 ** 1.825 ** 1.819 ** 1.817 ** 1.804 **

2nd − 3rd Grade Slope 0.351 ** 0.353 ** 0.353 ** 0.351 ** 0.353 ** 0.351 ** 0.351 **


1st Grade Slope 0.356 ** 0.359 ** 0.354 ** 0.320 ** 0.331 ** 0.306 ** 0.301 **

2nd − 3rd Grade Slope 0.038 ** 0.043 ** 0.042 ** 0.038 ** 0.042 ** 0.038 ** 0.038 **


at kindergarten entry as well. Although the coefficient is smaller than that for so-

cial class, the 2.7 points per standard deviation, implying an effect size of .32,

captures sizeable advantage/disadvantage. Children differing by two standard de-

viations are expected to begin school with a sizeable reading score difference of .64

standard deviations or 5.4 points.

Concerted cultivation is also an important mediator, reducing the black dis-

advantage (model D) by 80%, resulting in a non-significant race gap. Recently

Fryer and Levitt (2004) reported eliminating the black-white reading gap with

11 covariates; however, it appears that accounting for differing levels of concerted

cultivation is sufficient to explain the gap at kindergarten entry. The Hispanic-

white difference is reduced by nearly 44%, although the residual 2-point difference

remains statistically significant.5 In similar fashion to the mathematics models,

Asian children from families with the same level of concerted cultivation as white

children score significantly higher than white children from families with compara-

5Fryer and Levitt (2004) do not explain the Hispanic-white difference completely either, al-though they explain more of the difference with a much larger covariate list (in comparison tomodel G).

166

ble levels of concerted cultivation, the coefficient increases from an approximately

1-point advantage to over 3 points.

The social class association with initial status is mediated by concerted culti-

vation too, although the sizeable 22% reduction is smaller than the reduction for

either black or Hispanic children. Adjusting for concerted cultivation in model E

reduces the class effect from 3.2 points or .37 standard deviations to less than 2.5

points or .29 standard deviations. Even after adjusting for concerted cultivation,

the social class effect remains large. Concerted cultivation is reduced in model E

by about 33% when social class is added to the equation.

Over increasingly complex model specifications, the black coefficient remains

non-significant, although it becomes small and positive. The Hispanic-white dif-

ference is reduced to a little over a single point with the addition of social class

and whether or not a non-English language is spoken in the home, which means

the Hispanic-white difference decreases to below .15 standard deviations in magni-

tude. The Asian advantage, however, is impervious to the control list at 2.9 points

or .35 standard deviations in the most complex specification. In model G social

class persists as the strongest predictor of children’s reading skills, although the

concerted cultivation association remains sizeable as well, predicting that children

who differ by one standard deviation will have test scores that differ by 1.5 points

or .17 standard deviations while the comparable social class difference is over .23

standard deviations. Although the Asian-white disparity is large, social standing

and concerted cultivation are more powerful predictors of children’s reading skills

at kindergarten entry than racial/ethnic status.

The Kindergarten Slope

Black and Hispanic children grow slower than white children over the course of the

kindergarten year by .26 and .12 points, respectively (model A). Adding concerted

cultivation (model D) mediates the black-white difference in points acquired per

month by 19%, and reduces the Hispanic-white difference to non-significance (a

reduction of 57%). Asian children grow at a faster rate during kindergarten (model

A), and accrue even larger advantages when concerted cultivation is added to the

equation, the growth advantage increasing from .12 points per month to over .19,

a 60% increase.

167

Concerted cultivation is not strongly implicated in the .14 point per month

social class advantage (model B), reducing the class coefficient by only 15% (model

E). Higher social class and Asian children grow faster across model specifications,

while black children acquire reading skills at slower rates over the school year. The

concerted cultivation coefficient, while being associated with increased performance

in model C, does not persist as an important predictor across model specifications.

The Summer Slope

Although white, black, and Hispanic children on average lose the same amount of

ground over the summertime, between .15 and .34 points depending on the model

specification, Asian children’s reading achievement grows at a rate of (.69-.20=)

.49 points per month. This large Asian increment suggests important influences

which operate outside of the formal education system and are not explained by

social class and parenting strategies such as concerted cultivation, as evinced by

the increasing Asian advantage across model specifications, particularly when con-

certed cultivation is added, perhaps because Asian parents utilize different patterns

of investment in their children than other parents (Sun 1998). Social class is also

significantly associated with children’s summertime learning (model B), operating

independently of concerted cultivation (model E). Although concerted cultivation

is associated with children’s summertime growth in model C, it does not persist

as an important mediator in its own right across model specifications. Rather, it

appears that concerted cultivation proxies for social class during the summertime.

Although the social class advantage appears large in relative terms (+.21 points),

given the large standard deviation for the summer slope (about√

3.4 = 1.8), the

effect size is only .11, whereas the Asian-white difference in standard deviations is

over .4 standard deviations. Understanding this source of Asian advantage is an

important goal for future research.

The 1st Grade Slope

All race/ethnic groups have average first grade learning rates that are lower than

whites’ (model A). Black and Hispanic children both lose approximately .4 fewer

points per month. Including concerted cultivation (model D) reduces the black

168

coefficient by 22% and the Hispanic coefficient by 29%, and although both remain

statistically significant, the overall coefficient magnitude is sizeable, while reduc-

ing the Asian-white deficit to non-significance (70%). The fact that concerted

cultivation had a suppressor relationship with Asian advantage on the previous

growth parameters but now plays a mediating role is surprising, and it is not clear

why the relationship between these covariates would change over this particular

developmental period.

Children of different social classes acquire reading knowledge at disparate rates.

A two standard deviation difference between children is expected to result in

growth rates that are approximately the size of the black-white difference, both

before and after adjusting for concerted cultivation. Between models B and E

the social class coefficient is reduced by 24%, although the magnitude remains

sizeable, .16 additional points per month for each standard deviation difference in

social class.

Higher growth rates for children from advantaged concerted cultivation families

are persistent across models and of a similar magnitude to social class, although

slightly smaller, by model G. The black- and Hispanic-white differences continue

to be sizeable even after the full covariate list is included in model G, indicating

divergence in test scores by race, social class, and concerted cultivation during the

first grade even after adjusting for the full covariate list.

The 2nd − 3rd Grade Slope

White children grow faster than other children over the second and third grade

years (model A), with Asian children growing the slowest (.24 point decrement),

followed by black children (-.16 point decrement), and Hispanic children (.05 point

decrement). These disparities are persistent across model specifications and unre-

lated to concerted cultivation. Children over this time period are not differentiated

by social class nor consistently by concerted cultivation, although race/ethnic gaps

are large, particularly for Asian children, although Hispanic and black children fall

consistently behind over this period.

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Summary

The results presented in the previous sections are depicted in figures 7.1 to 7.6.

Growth and difference-from-whites’ curves by race are presented in figures 7.1 and

7.2. The race figures illustrate the divergence of black and white children’s reading

skills over time. Although concerted cultivation completely mediates black chil-

dren’s differences in reading achievement scores at kindergarten entry and reduces

gaps in kindergarten and first grade learning rates, differences accumulate over the

school years, but not summer, as depicted in figures 7.1 and 7.2. The black-white

difference at the end of third grade is 11.1 points prior to adjusting for concerted

cultivation, and 8.4 afterwards, a 24% reduction.6 In standard deviations, the dif-

ference decreases from .66 to .4, which suggests that in the metric of the test, the

black-white gap increases significantly over the first years of school (from 0 to .4

standard deviations) which means that systematic black-white differences in test

scores once children enter school are not related to concerted cultivation.

There is also evidence for Hispanic-white divergence, which is most clear in

figure 7.2, although the difference is significantly less than for the black-white

case. Prior to adjusting for concerted cultivation, the expected Hispanic-white

difference is decreased from 8.9 points (.53 standard deviations) to 5.2 points (.31

standard deviations) afterwards, a 42% reduction in the absolute difference which

suggests that concerted cultivation plays an important role in Hispanic-white read-

ing disparities. The pattern of the Asian-white difference is complex, showing early

divergence and a marked tendency towards convergence beginning in first grade,

but more strongly over the second and third grade years. Over the second and

third grade periods, Asian children lose a good deal of ground relative to white

children.

Growth and difference curves for social class plotted at the mean and ±1 stan-

dard deviation are presented in figures 7.3 and 7.4. As suggested by the coefficients

reported in table 7.2 and both figures, social class differences in growth rates are

significant previous to the end of first grade, after which children no longer con-

tinue to diverge by social class. Prior to controlling for concerted cultivation, the

6Estimated from models with the time variables recoded so that the model intercept appliesto the end of third grade. The standard deviations of the intercept is 16.8. These results are notshown.

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Figure 7.3. Graphical Depictions of Children’s Math Growth from Kindergarten EntryThrough 3rd Grade by Social Class from Table 7.2

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Figure 7.4. Graphical Depictions of Social Class Differences in Children’s Math Growthfrom Kindergarten Entry Through 3rd Grade from Table 7.2

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C. Cult. Reading Achievement Difference

social class gradient is expected to be 7.2 points per standard deviation at the end

of third grade, and 5.5 points afterward, a difference reduction of approximately

25%. This reduction is visible in both figures, but is captured most dramatically

in figure 7.4.

Children from families with differing concerted cultivation levels also appear to

have growing test score gaps over time (see figure 7.5), although the divergence is

largely explained by compositional differences correlated with concerted cultivation

(see Appendix table A.12). The reduction in growth differences is captured in figure

7.6, which highlights the fact that although initial differences are not explained,

differences in growth over the kindergarten and second and third grade years are

explained. Children continue to diverge over the first grade year, however, and

concerted cultivation is notably unrelated to summertime growth.


Covariates are often correlated with the random effects in multilevel models, which


the original model set in table 7.2. That the covariates might be correlated with

176

the random effects is not surprising when one considers unequal access to educa-

tional opportunities across social groups, which we have previously encountered in

the general knowledge and math models. For this reason, the next series of models

replicate those in table 7.2 but with the covariates centered around their respective

group-means. These models are, in affect, growth models with school-level fixed

effects, which means the between student parameters represent average expected

math achievement scores between children who attend the same school. In ad-

dition, this approach has the added benefit of adjusting for constant school-level

confounders. Results are presented in table 7.37 and summarized in figures 7.7 to

7.12.

Initial Status

As with the math models, the average within-school effects estimated via the

school-level “fixed-effects” models are smaller since the original estimates were

biased by unobserved between-school relationships between the regressors and out-

come. However, the black coefficient is again fully mediated with the inclusion of

concerted cultivation (models A and D, a reduction of 64%). The Hispanic coef-

ficient is also reduced by approximately 40%, whereas the Asian-white difference

becomes dramatically larger—increasing by a magnitude of nearly 2.7 times when

children in the same schools are compared.

The social class association is also smaller when the average within-school effect

is estimated, although children differing by one standard deviation are expected to

have score differences of 2.5 points or .29 standard deviations (model B). Including

concerted cultivation reduces the association to 2 points (model E), an attenua-

tion of 21%. Concerted cultivation persists as a significant predictor of children’s

reading skills at kindergarten entry across model specifications, albeit of a magni-

tude somewhat smaller than social class. Given the high correlation between social

class and concerted cultivation, these two variables combined imply significantly

different skills between children in the same schools at kindergarten entry.


177

Table 7.3. Group-Mean Centered Growth Models for Reading Achievement Scores (IRT) fromKindergarten Entry Until Spring of Third Grade by Race, Social Class, Concerted Cultivation,and Selected Covariates (ECLS-K)

Model


Initial Status 23.217 ** 23.226 ** 23.236 ** 23.233 ** 23.234 ** 23.229 ** 23.220 **(0.162) (0.161) (0.162) (0.162) (0.161) (0.161) (0.162)

Black −1.724 ** −0.624 −0.254 −0.169(0.370) (0.368) (0.365) (0.373)

Hispanic −2.957 ** −1.774 ** −1.362 ** −1.113 **(0.308) (0.309) (0.306) (0.316)

Asian 1.099 * 2.707 ** 2.274 ** 2.625 **(0.434) (0.428) (0.425) (0.458)

Other Race −0.886 * −0.207 −0.039 0.091(0.444) (0.439) (0.434) (0.432)

Social class 2.506 ** 1.982 ** 1.875 ** 1.626 **(0.106) (0.111) (0.112) (0.116)


Second K. 1.141 * 1.532 ** 1.360 ** 1.464 ** 1.690 ** 1.754 ** 2.015 **(0.452) (0.447) (0.450) (0.446) (0.444) (0.442) (0.440)

Age at K. Entry 0.303 ** 0.314 ** 0.301 ** 0.299 ** 0.307 ** 0.306 ** 0.319 **(0.020) (0.020) (0.020) (0.020) (0.020) (0.020) (0.020)


Black −0.237 ** −0.195 ** −0.173 ** −0.155 **(0.045) (0.046) (0.046) (0.047)

Hispanic −0.107 ** −0.051 −0.025 −0.028(0.039) (0.039) (0.039) (0.041)

Asian 0.115 * 0.184 ** 0.159 ** 0.146 *(0.054) (0.055) (0.055) (0.059)

Other Race −0.071 −0.043 −0.033 −0.023(0.056) (0.056) (0.056) (0.056)

Social class 0.138 ** 0.119 ** 0.111 ** 0.095 **(0.014) (0.014) (0.014) (0.015)

Concerted Cultivation 0.091 ** 0.089 ** 0.052 ** 0.055 ** 0.030 *(0.013) (0.014) (0.014) (0.015) (0.015)

Second K. −0.335 ** −0.306 ** −0.327 ** −0.324 ** −0.303 ** −0.303 ** −0.275 **(0.058) (0.058) (0.059) (0.058) (0.058) (0.058) (0.058)

Changed School −0.168 −0.154 −0.127 −0.122 −0.126 −0.121 −0.099(0.091) (0.091) (0.091) (0.091) (0.091) (0.090) (0.090)

Summer Slope −0.180 ** −0.180 ** −0.180 ** −0.179 ** −0.180 ** −0.179 ** −0.180 **(0.056) (0.056) (0.056) (0.056) (0.056) (0.056) (0.056)

Black 0.199 0.267 0.305 0.285(0.175) (0.176) (0.177) (0.180)

Hispanic 0.166 0.236 0.270 0.292(0.159) (0.161) (0.161) (0.166)

Asian 0.721 ** 0.825 ** 0.792 ** 0.797 **(0.224) (0.227) (0.227) (0.241)

Other Race 0.333 0.374 0.388 0.373(0.212) (0.213) (0.213) (0.214)

Social class 0.200 ** 0.191 ** 0.187 ** 0.194 **(0.055) (0.058) (0.058) (0.060)

Concerted Cultivation 0.086 0.130 * 0.026 0.072 0.041(0.053) (0.055) (0.056) (0.057) (0.060)

Second K. −0.206 −0.152 −0.187 −0.191 −0.150 −0.156 −0.126(0.234) (0.233) (0.233) (0.234) (0.233) (0.234) (0.234)

Changed School 0.000 −0.019 0.014 0.014 −0.006 −0.004 −0.001(0.137) (0.137) (0.137) (0.137) (0.137) (0.136) (0.136)

1st Grade Slope 3.363 ** 3.364 ** 3.366 ** 3.366 ** 3.367 ** 3.367 ** 3.368 **(0.029) (0.029) (0.029) (0.029) (0.029) (0.029) (0.029)

Black −0.263 ** −0.198 ** −0.177 * −0.167 *(0.074) (0.074) (0.074) (0.075)

Hispanic −0.200 ** −0.117 −0.088 −0.108(0.065) (0.065) (0.065) (0.067)

Asian −0.055 0.046 0.016 −0.013(0.090) (0.091) (0.091) (0.096)

Other Race −0.061 −0.021 −0.006 0.004(0.090) (0.090) (0.090) (0.090)

Social class 0.161 ** 0.126 ** 0.119 ** 0.092 **(0.023) (0.024) (0.024) (0.025)


Second K. −0.505 ** −0.479 ** −0.496 ** −0.491 ** −0.474 ** −0.471 ** −0.423 **(0.092) (0.092) (0.092) (0.092) (0.092) (0.092) (0.091)

Changed School −0.377 ** −0.326 ** −0.370 ** −0.372 ** −0.331 ** −0.336 ** −0.298 *(0.117) (0.116) (0.117) (0.116) (0.116) (0.116) (0.115)


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Table 7.3 – Continued: Reading Achievement, Group-Mean CenteredModel


2nd − 3rd Grade Slope 1.577 ** 1.578 ** 1.578 ** 1.577 ** 1.578 ** 1.577 ** 1.577 **(0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009)

Black −0.115 ** −0.110 ** −0.111 ** −0.118 **(0.027) (0.027) (0.027) (0.027)

Hispanic −0.016 −0.011 −0.011 −0.015(0.022) (0.023) (0.023) (0.024)

Asian −0.206 ** −0.200 ** −0.198 ** −0.203 **(0.030) (0.031) (0.031) (0.033)

Other Race −0.076 * −0.075 * −0.076 * −0.077 *(0.033) (0.033) (0.033) (0.033)

Social class 0.000 −0.007 −0.004 −0.015(0.008) (0.008) (0.008) (0.009)

Concerted Cultivation 0.019 * 0.008 0.021 * 0.010 0.001(0.008) (0.008) (0.008) (0.008) (0.009)

Second K. −0.053 −0.054 −0.051 −0.052 −0.052 −0.052 −0.047(0.034) (0.034) (0.034) (0.034) (0.034) (0.034) (0.034)

Changed School −0.047 ** −0.052 ** −0.046 ** −0.042 * −0.046 ** −0.042 ** −0.037 *(0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016)

Level-2 Variance ComponentsIntercept 68.788 ** 65.889 ** 66.701 ** 66.036 ** 64.614 ** 64.198 ** 62.692 **Kindergarten Slope 0.866 ** 0.859 ** 0.865 ** 0.861 ** 0.857 ** 0.854 ** 0.846 **Summer Slope 3.368 ** 3.377 ** 3.388 ** 3.360 ** 3.374 ** 3.346 ** 3.336 **1st Grade Slope 1.832 ** 1.823 ** 1.826 ** 1.821 ** 1.818 ** 1.814 ** 1.800 **2nd-3rd Grade Slope 0.351 ** 0.353 ** 0.353 ** 0.351 ** 0.353 ** 0.351 ** 0.351 **

Level-3 Variance ComponentsIntercept 17.976 ** 18.010 ** 18.032 ** 18.077 ** 18.084 ** 18.131 ** 18.268 **Kindergarten Slope 0.192 ** 0.193 ** 0.193 ** 0.192 ** 0.193 ** 0.193 ** 0.193 **Summer Slope 0.496 ** 0.496 ** 0.493 ** 0.496 ** 0.495 ** 0.498 ** 0.497 **1st Grade Slope 0.434 ** 0.432 ** 0.431 ** 0.431 ** 0.430 ** 0.430 ** 0.430 **2nd-3rd Grade Slope 0.043 ** 0.043 ** 0.043 ** 0.043 ** 0.043 ** 0.043 ** 0.043 **



Although coefficient magnitudes on the initial status decrease when adjusting for

differences in school means, the results for the kindergarten slope are remarkably

similar to the non-centered models, indicating that parameter estimates in table 7.2

are not significantly biased by between-school compositional relationships. During

the kindergarten year, the black coefficient is mediated by approximately 17%,

and the Hispanic coefficient is again reduced to non-significance when concerted

cultivation is adjusted for, while the Asian advantage increases (models A and

D). Again, social class is shown to be an influential predictor of children’s read-

ing growth over kindergarten, and in fact, has a slightly larger effect in model G.

In the non-group-mean centered models, concerted cultivation was not associated

with children’s growth in model G, while in the centered versions, concerted culti-

vation has a small, statistically significant relationship across model specifications,

suggesting a slight tendency for dispersion over this time period.

Concerted cultivation is not associated with summer learning, and as a result,

contributes little to our understanding of social class differences in learning rates

179

during this period. The Asian summer advantage is even larger in the centered

models. This finding is surprising at first; however, the Asian-white difference

favoring Asian children tends to increase as children’s backgrounds are equated to

those of white children, so increases resulting from contextual equating is not as

unexpected as it was with the data presented in earlier chapters.

Coefficient estimates are smaller during the first grade year in the centered

than non-centered models, and the reduction is larger for race than social class

and concerted cultivation, suggesting that between-school contextual factors are

related to average levels of achievement within schools. Average within-school

black-white differences in first grade learning rates are reduced by 22%, Hispanic-

white differences by 40%, and reduced to non-significance (models A and D). Unlike

the non-centered models, when children in the same schools are compared, there

is no Asian-white difference evinced. Concerted cultivation mediates over 24% of

the social class association, and remains a persistent predictor of children’s first

grade learning rates across model specifications.

In agreement with the previous results, black and Asian children’s lower learn-

ing rates during the second and third grade years persist across models and are

not related to either concerted cultivation or social class. When constant school

level factors are taken into account, however, Hispanic children are not statistically

differentiable from white children.

Summary

The fixed-effects models in table 7.3 are largely consistent with the population-

based analysis presented in table 7.2. At the end of third grade, the average

test-score difference between black and white children in the same schools is 8.4

points. After adjusting for differences in concerted cultivation, the difference is

approximately 6 points, a reduction of nearly 30%. Although the reduction is

clear in figures 7.7 and 7.8, it is also clear that white and black children’s test

scores rapidly diverge over time. This does not appear to be the case for Hispanic

children, whose trajectory is parallel to that of whites’ over the last two years

of the study. The reduction in the Hispanic-white difference at the end of third

grade is also apparent in the figures, and is about 49% (5.7 points versus 2.9 points

after adjusting for concerted cultivation). As previously noted, the Asian-white

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Figure 7.9. Graphical Depictions Children’s Math Growth from Kindergarten EntryThrough 3rd Grade by Social Class from Table 7.3

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Figure 7.10. Graphical Depictions of Social Class Differences in Children’s MathGrowth from Kindergarten Entry Through 3rd Grade from Table 7.3

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difference follows a more complex pattern, rising through first grade, and is in fact

larger when concerted cultivation is controlled, but diminishes rapidly over the

course of the second and third grades. When concerted cultivation is adjusted for,

Asian children are expected to end the third grade with a 2.4 point advantage,

although if within-person trends continue, Asian children’s scores are expected to

drop below those for white children in subsequent grades.

Children’s reading skills by social class also diverge rapidly through the end of

first grade, but flatten out during the second and third grades. The social class

gradient at the end of third grade is approximately 5.6 points, which decreases

to 4.6 points when concerted cultivation is adjusted for (a reduction of less than

20%; see figures 7.9 and 7.10). The pattern in figures 7.11 and 7.12 is similar for

concerted cultivation, although the divergence is smaller. After adjusting for race

and social class, however, the divergence is greatly reduced, although the gradient

decreases from 5 points to 3.5 points, a difference which is still relatively large in

magnitude.

186





dition to the concerted cultivation coefficients. These models were principally

concerned with the (1) the extent to which concerted cultivation mediated race

and class differences, and (2) the average relationship between concerted culti-

vation and children’s growth trajectories across social groups. The next series of

models allow concerted cultivation to vary across racial and ethnic categorizations.

Does concerted cultivation function equally for groups of differing social status and

race/ethnic identification? Results are reported in table 7.4 for race interactions,

table 7.5 for the social class models, and race by class by concerted cultivation

interactions are presented in table 7.6.


Results for models showing race and concerted cultivation interactions are reported

in table 7.4. Model A estimates race and concerted cultivation interactions prior

to controlling for social class, and model B adds social class. Models C and D

are replicates of models A and B, using group-mean centering to eliminate unmea-

sured contextual factors. Consistent with the previous discussion, the coefficients

reported in models C and D are average within-school effects based upon children

who attend the same schools.8






the increments or decrements in the expected difference for the specific group from

whites. For example, the negative coefficient for black children represents a lower


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Table 7.4. Race Interactions with Concerted Cultivation Growth Models for Reading Achieve-ment Scores (IRT) from Kindergarten Entry Until the Spring of Third Grade (ECLS-K)


Variables A B C D

Initial Status 23.563 ** 23.370 ** 23.228 ** 23.224 **(0.158) (0.144) (0.162) (0.162)

Black −0.938 ** −0.242 −1.021 ** −0.605(0.325) (0.315) (0.387) (0.383)

Hispanic −2.007 ** −1.440 ** −1.827 ** −1.430 **(0.277) (0.270) (0.312) (0.309)

Asian 3.751 ** 3.197 ** 3.298 ** 2.840 **(0.408) (0.402) (0.441) (0.439)

Other −0.843 * −0.778 * −0.235 −0.073(0.393) (0.381) (0.441) (0.436)

Social Class 2.320 ** 1.861 **(0.102) (0.112)


Interactions W/ C. CultivationBlack −1.002 ** −0.806 ** −0.813 ** −0.686 *

(0.290) (0.284) (0.307) (0.302)Hispanic 0.065 0.040 −0.011 −0.028

(0.267) (0.262) (0.279) (0.276)Asian 1.969 ** 1.925 ** 1.843 ** 1.793 **

(0.379) (0.374) (0.393) (0.391)Other 0.026 0.138 −0.171 −0.139


(0.021) (0.021) (0.018) (0.018)Black −0.222 ** −0.192 ** −0.211 ** −0.187 **

(0.041) (0.041) (0.048) (0.048)Hispanic −0.045 −0.015 −0.043 −0.018

(0.036) (0.036) (0.040) (0.040)Asian 0.187 ** 0.164 ** 0.178 ** 0.151 **

(0.053) (0.053) (0.057) (0.057)Other −0.014 −0.001 −0.035 −0.026

(0.051) (0.051) (0.056) (0.056)Social Class 0.103 ** 0.110 **

(0.013) (0.014)Concerted Cultivation 0.106 ** 0.068 ** 0.108 ** 0.073 **

(0.017) (0.018) (0.018) (0.019)Interactions W/ C. CultivationBlack −0.061 −0.055 −0.076 * −0.070

(0.037) (0.037) (0.038) (0.038)Hispanic −0.029 −0.030 −0.028 −0.028

(0.034) (0.034) (0.036) (0.036)Asian −0.087 −0.089 −0.087 −0.089

(0.048) (0.048) (0.049) (0.049)Other 0.007 0.010 0.025 0.027

(0.049) (0.048) (0.050) (0.050)Summer Slope −0.213 ** −0.226 ** −0.180 ** −0.180 **

(0.073) (0.074) (0.056) (0.056)Black 0.178 0.234 0.289 0.328

(0.149) (0.150) (0.184) (0.184)Hispanic 0.142 0.188 0.182 0.216

(0.141) (0.142) (0.162) (0.162)Asian 0.907 ** 0.857 ** 0.938 ** 0.902 **

(0.223) (0.223) (0.238) (0.239)Other 0.047 0.050 0.357 0.371

(0.181) (0.181) (0.214) (0.214)Social Class 0.208 ** 0.188 **

(0.053) (0.058)Concerted Cultivation 0.086 0.005 0.071 * 0.012


(0.142) (0.142) (0.150) (0.150)Hispanic 0.007 0.010 −0.010 −0.008

(0.133) (0.133) (0.142) (0.142)Asian 0.332 0.329 0.381 0.378

(0.204) (0.204) (0.209) (0.209)Other 0.178 0.191 0.113 0.116

(0.170) (0.169) (0.176) (0.175)


188

Table 7.4 – Continued: Reading Achievement, Race Interactions


Variables A B C D

1st Grade Slope 3.468 ** 3.456 ** 3.367 ** 3.367 **(0.034) (0.034) (0.029) (0.029)

Black −0.394 ** −0.353 ** −0.239 ** −0.214 **(0.066) (0.066) (0.077) (0.078)

Hispanic −0.225 ** −0.187 ** −0.080 −0.051(0.059) (0.059) (0.066) (0.067)

Asian −0.088 −0.121 0.009 −0.022(0.090) (0.090) (0.096) (0.095)

Other −0.111 −0.100 −0.001 0.014(0.081) (0.081) (0.090) (0.090)

Social Class 0.143 ** 0.118 **(0.022) (0.024)



(0.060) (0.059) (0.062) (0.062)Hispanic 0.030 0.029 −0.008 −0.009

(0.053) (0.053) (0.056) (0.056)Asian −0.143 −0.143 −0.193 * −0.194 *

(0.079) (0.079) (0.082) (0.082)Other −0.079 −0.075 −0.100 −0.099

(0.076) (0.076) (0.079) (0.078)Additional ControlsSecond Time K. −0.485 ** −0.463 ** −0.491 ** −0.471 **

(0.088) (0.088) (0.092) (0.092)Changed Schools −0.416 ** −0.373 ** −0.368 ** −0.332 **

(0.114) (0.113) (0.116) (0.116)2nd − 3rd Grade Slope 1.635 ** 1.636 ** 1.577 ** 1.578 **

(0.011) (0.011) (0.009) (0.009)Black −0.149 ** −0.148 ** −0.114 ** −0.115 **

(0.023) (0.023) (0.028) (0.028)Hispanic −0.032 −0.031 −0.004 −0.005

(0.020) (0.020) (0.023) (0.023)Asian −0.249 ** −0.248 ** −0.216 ** −0.215 **

(0.029) (0.029) (0.032) (0.032)Other −0.131 ** −0.133 ** −0.077 * −0.077 *

(0.029) (0.029) (0.033) (0.033)Social Class −0.002 −0.004

(0.008) (0.008)Concerted Cultivation 0.000 0.000 0.005 0.006

(0.010) (0.010) (0.011) (0.011)Interactions W/ C. CultivationBlack 0.014 0.014 0.000 −0.001

(0.021) (0.021) (0.022) (0.022)Hispanic 0.030 0.030 0.026 0.026

(0.018) (0.018) (0.020) (0.020)Asian −0.023 −0.023 −0.029 −0.029

(0.026) (0.026) (0.027) (0.027)Other 0.030 0.032 0.011 0.012

(0.028) (0.028) (0.030) (0.030)


189

return to concerted cultivation than white children experience. At kindergarten

entry, black children gain only (1.61-.69=) .9 points for each standard deviation

increase in concerted cultivation, compared to white and Hispanic children who, on

average, receive 1.6 points (model C). This finding is contrary to the expectations

drawn from Lareau (2003), who found that concerted cultivation operated simi-

larly for white and black children, although Farkas and Beron (2004) report similar

findings by social class and the HOME inventories9 (see also Sun 1998). This find-

ing is similar to that found previously for general knowledge and mathematics, and

it will receive more attention in section 7.2.3. Asian children, on the other hand,

receive over double the return from concerted cultivation as white and Hispanic

children, which is not surprising given the tendency of Asians to outperform white

children with similar levels of concerted cultivation found in earlier analyses.

There are no consistent interaction patterns during kindergarten or over the

summertime or second and third grade years, although the black by concerted

cultivation re-emerges during the first grade. During the first grade year, both

Asian and black children alike suffer decrements of similar magnitude. In fact, the

deficit is larger than the concerted cultivation main effect, implying a tendency

for children from higher concerted cultivation to grow more slowly during the

first grade while less advantaged children grow faster—pointing to a tendency

for advantaged and disadvantaged black and Asian children to converge over this

period.


The previous section addressed the question of whether or not concerted cultiva-

tion relates to children’s general knowledge achievement differently by race/ethnic

group. This section is conceptually similar, although the question is posed for

social class instead. Although social class was used as a standardized, continuous

covariate in the preceding analyses, in the following section social class is coded

as a series of dummy variables. By coding social class as a series of categorical

indicators, the interpretation of the interactions is similar to those in the race in-

teraction models. The first grouping, low social class, captures the bottom quartile

9They show this relation with the PPVT for social class, mothers verbal AFQT, and cognitivestimulation (pg. 484, Table 4).

190

Table 7.5. Social Class Interactions with Concerted Cultivation Growth Models for ReadingAchievement Scores (IRT) from Kindergarten Entry Until the Spring of Third Grade (ECLS-K)


Variables A B C D

Initial Status 22.888 ** 23.004 ** 23.261 ** 23.256 **(0.143) (0.162) (0.161) (0.161)

Black −0.009 −0.363(0.293) (0.364)

Hispanic −1.541 ** −1.458 **(0.267) (0.307)

Asian 2.723 ** 2.419 **(0.389) (0.426)

Other −0.749 m −0.040(0.383) (0.435)

Low SES (< −25%) −2.431 ** −2.316 ** −1.953 ** −1.842 **(0.265) (0.266) (0.277) (0.277)

High SES (> +25%) 3.368 ** 3.136 ** 2.594 ** 2.403 **(0.248) (0.249) (0.260) (0.261)


Interactions W/ C. CultivationLow SES (< −25%) −0.593 * −0.625 * −0.488 m −0.531 *

(0.253) (0.253) (0.260) (0.259)High SES (> +25%) 0.379 0.465 0.415 0.486 m


(0.020) (0.023) (0.018) (0.018)Black −0.184 ** −0.180 **

(0.039) (0.046)Hispanic −0.022 −0.026

(0.035) (0.039)Asian 0.174 ** 0.162 **

(0.051) (0.055)Other −0.014 −0.036

(0.051) (0.056)Low SES (< −25%) −0.121 ** −0.105 ** −0.100 ** −0.089 *

(0.035) (0.035) (0.036) (0.036)High SES (> +25%) 0.161 ** 0.144 ** 0.172 ** 0.158 **

(0.032) (0.032) (0.033) (0.033)Concerted Cultivation 0.071 ** 0.067 ** 0.067 ** 0.067 **


(0.033) (0.033) (0.033) (0.033)High SES (> +25%) −0.083 ** −0.075 * −0.075 * −0.070 *

(0.032) (0.032) (0.033) (0.033)Summer Slope −0.161 * −0.260 ** −0.178 ** −0.177 **

(0.070) (0.081) (0.056) (0.056)Black 0.199 0.307

(0.143) (0.177)Hispanic 0.245 0.266

(0.139) (0.161)Asian 0.783 ** 0.801 **

(0.210) (0.227)Other 0.071 0.394

(0.180) (0.213)Low SES (< −25%) −0.263 * −0.279 * −0.263 m −0.277 *

(0.133) (0.134) (0.139) (0.140)High SES (> +25%) 0.362 ** 0.338 ** 0.300 * 0.284 *

(0.125) (0.126) (0.133) (0.133)Concerted Cultivation 0.115 0.161 * 0.087 0.131

(0.071) (0.072) (0.075) (0.076)Interactions W/ C. CultivationLow SES (< −25%) −0.100 −0.107 −0.062 −0.073

(0.127) (0.127) (0.130) (0.130)High SES (> +25%) −0.214 −0.193 −0.168 −0.150

(0.126) (0.126) (0.131) (0.131)


191

Table 7.5 – Continued: Reading Achievement, SES Interactions


Variables A B C D

1st Grade Slope 3.407 ** 3.492 ** 3.368 ** 3.368 **(0.032) (0.037) (0.029) (0.029)

Black −0.301 ** −0.184 *(0.062) (0.074)

Hispanic −0.223 ** −0.082(0.057) (0.065)

Asian −0.084 0.021(0.085) (0.091)

Other −0.117 −0.012(0.080) (0.090)

Low SES (< −25%) −0.228 ** −0.204 ** −0.157 ** −0.147 *(0.056) (0.056) (0.058) (0.058)

High SES (> +25%) 0.181 ** 0.167 ** 0.143 * 0.135 *(0.053) (0.053) (0.055) (0.056)

Concerted Cultivation 0.125 ** 0.105 ** 0.085 ** 0.081 *(0.030) (0.030) (0.031) (0.031)

Interactions W/ C. CultivationLow SES (< −25%) 0.066 0.069 0.068 0.069

(0.052) (0.052) (0.053) (0.053)High SES (> +25%) 0.006 0.008 0.003 0.003

(0.053) (0.053) (0.055) (0.055)2nd − 3rd Grade Slope 1.590 ** 1.635 ** 1.578 ** 1.578 **

(0.011) (0.012) (0.009) (0.009)Black −0.143 ** −0.108 **

(0.022) (0.027)Hispanic −0.033 −0.008

(0.020) (0.023)Asian −0.231 ** −0.201 **

(0.028) (0.031)Other −0.128 ** −0.072 *

(0.029) (0.033)Low SES (< −25%) −0.033 −0.027 −0.028 −0.027

(0.020) (0.020) (0.020) (0.020)High SES (> +25%) 0.003 0.008 0.003 0.010

(0.018) (0.018) (0.019) (0.019)Concerted Cultivation 0.029 ** 0.008 0.023 * 0.011

(0.010) (0.010) (0.011) (0.011)Interactions W/ C. CultivationLow SES (< −25%) −0.016 −0.007 −0.021 −0.015

(0.018) (0.018) (0.019) (0.019)High SES (> +25%) −0.011 −0.011 −0.006 −0.010

(0.018) (0.018) (0.019) (0.019)


of the distribution; average social class, the reference category, captures the middle

50% of the distribution, and high social class is composed of the top quartile. The

results presented in table 7.5 follow a parallel progression to those presented in

table 7.4.

Similar to the previous findings for black children, lower social class children

receive, on the one hand, lower returns on concerted cultivation, but on the other

hand, receive fewer detriments from concerted cultivation disadvantage. The lower

concerted cultivation slope of (1.74-.53=) 1.21 points suggests that lower social

192

class children still receive benefits. The only other significant interaction occurs

during the kindergarten year when higher social class children do not receive the

same benefit from concerted cultivation that other children do (.067− .07 ≈ 0).




distribution (‘A’ models), the middle 50% (‘B’ models), and the top quartile (‘C’




perplexing too (see also Farkas and Beron 2004). Results from table 7.6 illustrate

which black children are benefitting, and which are not.

The lower return to concerted cultivation for black children reported in table

7.4 is driven primarily by by the negative decrement for lower social class chil-

dren. Given the distribution of scores for this group reported in figure 4, most

of this group of children have negative values on the measure, which means that

many lower-class black children are doing better than expected compared to white

children of the same social class and concerted cultivation backgrounds. Although

not significant, the other black by concerted cultivation interactions are negative

in magnitude, which also mirrors the result for the general knowledge and math-

ematics achievement tests. The negative interactions with concerted cultivation

for lower and middle class black children are also significant over the first grade,

indicating a null concerted cultivation effect for lower class black children. For

middle class black children, the tendency is towards either a null or small nega-

tive concerted cultivation association, suggesting convergence across the concerted

cultivation distribution during the first grade.

Asian children were previously shown to be more responsive to concerted culti-

vation than other children. This relationship is due to the strong positive increment

experienced by high social class Asian children. The Asian advantage is very large

for higher class children, nearly 4.5 points, but given the range of values over

the distribution of concerted cultivation (see figure 4), approximately half of the

193

Table 7.6. Race & Class Interactions with Concerted Cultivation Growth Models for ReadingAchievement Scores (IRT) from Kindergarten Entry Until the Spring of Third Grade (ECLS-K)

A-1 A-2 B-1 B-2 C-1 C-2

Initial Status 20.983 ** 20.719 ** 22.968 ** 21.819 ** 26.259 ** 21.886 **(0.259) (0.534) (0.170) (0.401) (0.378) (0.965)

Black −1.727 ** −1.593 ** 0.255 0.505 −0.568 0.449(0.467) (0.482) (0.373) (0.379) (0.987) (1.003)

Hispanic −2.441 ** −2.175 ** −1.267 ** −0.959 ** −1.679 −1.410(0.486) (0.520) (0.331) (0.344) (0.990) (0.995)

Asian 1.942 2.278 1.974 ** 1.997 ** 5.222 ** 4.472 **(1.197) (1.237) (0.594) (0.628) (0.864) (0.962)

Other −2.577 ** −2.326 ** −1.532 ** −1.126 * 0.000 −0.095(0.668) (0.662) (0.474) (0.465) (1.266) (1.241)

Social Class 0.464 * 2.045 ** 2.381 **(0.217) (0.300) (0.377)


Interactions W/ C. CultivationBlack −1.092 ** −0.935 * −0.702 −0.640 0.004 −0.335

(0.416) (0.410) (0.408) (0.402) (1.021) (1.000)Hispanic −0.550 −0.500 0.288 0.171 0.586 0.535

(0.443) (0.442) (0.381) (0.382) (1.072) (1.057)Asian 0.931 1.096 1.165 0.884 1.997 * 2.492 **

(0.849) (0.851) (0.624) (0.619) (0.849) (0.842)Other −0.516 −0.480 −0.274 −0.343 1.602 1.667

(0.642) (0.634) (0.506) (0.496) (1.162) (1.141)


Black −0.118 −0.089 −0.225 ** −0.193 ** −0.199 −0.164(0.074) (0.077) (0.053) (0.054) (0.117) (0.122)

Hispanic −0.101 −0.067 0.034 0.041 −0.073 −0.066(0.076) (0.084) (0.046) (0.048) (0.115) (0.117)

Asian −0.017 0.034 0.116 0.110 0.304 ** 0.237 *(0.188) (0.206) (0.083) (0.088) (0.099) (0.111)

Other −0.023 −0.008 0.005 0.022 0.091 0.084(0.108) (0.108) (0.067) (0.067) (0.146) (0.146)

Social Class 0.021 0.130 ** 0.163 **(0.034) (0.042) (0.045)

Concerted Cultivation 0.189 ** 0.157 ** 0.088 ** 0.039 0.014 −0.012(0.045) (0.046) (0.025) (0.026) (0.038) (0.040)

Interactions W/ C. CultivationBlack −0.075 −0.056 −0.140 * −0.128 * −0.050 −0.051

(0.067) (0.066) (0.056) (0.056) (0.118) (0.118)Hispanic −0.116 −0.103 −0.039 −0.044 −0.063 −0.051

(0.068) (0.069) (0.054) (0.054) (0.123) (0.123)Asian −0.319 * −0.301 * −0.010 0.002 −0.124 −0.100

(0.128) (0.130) (0.084) (0.085) (0.099) (0.100)Other −0.122 −0.106 0.024 0.026 −0.018 −0.018

(0.102) (0.101) (0.070) (0.070) (0.133) (0.133)

‘m’ p < .06, ‘*’p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.Note 1: Table continued on the following page.Note 2: ‘-1’ models include second time kindergartner, age at kindergarten entry (-66 months), and whether ornot the child changed schools over the given period. ‘-2’ models include the full covariate list.

194

Table 7.6 – Continued: Reading Achievement, Race & Social Class Interactions

A-1 A-2 B-1 B-2 C-1 C-2

Summer Slope −0.398 ** −0.436 m −0.230 * −0.271 * 0.016 −0.260(0.144) (0.225) (0.089) (0.123) (0.179) (0.317)

Black −0.168 −0.135 0.238 0.254 0.173 0.122(0.260) (0.265) (0.189) (0.195) (0.460) (0.467)

Hispanic −0.176 −0.198 0.152 0.182 0.105 0.105(0.270) (0.291) (0.182) (0.191) (0.426) (0.444)

Asian 1.502 1.369 0.427 0.429 1.712 ** 1.605 **(0.920) (0.946) (0.337) (0.350) (0.421) (0.460)

Other −0.331 −0.304 0.097 0.095 0.494 0.412(0.305) (0.306) (0.233) (0.235) (0.596) (0.597)

Social Class 0.167 0.200 0.150(0.133) (0.159) (0.181)

Concerted Cultivation 0.136 0.063 0.050 −0.014 0.005 −0.069(0.160) (0.162) (0.094) (0.098) (0.155) (0.162)

Interactions W/ C. CultivationBlack −0.204 −0.182 0.266 0.311 0.628 0.616

(0.240) (0.241) (0.213) (0.214) (0.477) (0.477)Hispanic −0.418 −0.336 0.030 0.008 0.467 0.453

(0.239) (0.243) (0.204) (0.208) (0.482) (0.489)Asian 0.592 0.644 0.567 0.545 −0.488 −0.483

(0.590) (0.585) (0.364) (0.371) (0.412) (0.418)Other −0.042 −0.022 0.290 0.282 −0.304 −0.256

(0.302) (0.301) (0.238) (0.239) (0.565) (0.565)1st Grade Slope 3.289 ** 3.458 ** 3.500 ** 3.517 ** 3.745 ** 3.702 **

(0.070) (0.104) (0.040) (0.053) (0.074) (0.126)Black −0.386 ** −0.375 ** −0.407 ** −0.382 ** −0.580 ** −0.515 **

(0.125) (0.127) (0.084) (0.085) (0.187) (0.190)Hispanic −0.329 ** −0.341 * −0.124 −0.137 −0.147 −0.082

(0.122) (0.131) (0.076) (0.080) (0.171) (0.176)Asian −0.141 −0.137 −0.049 −0.096 −0.349 * −0.252

(0.324) (0.335) (0.140) (0.146) (0.163) (0.179)Other −0.186 −0.163 −0.191 −0.183 −0.314 −0.249

(0.167) (0.167) (0.106) (0.106) (0.241) (0.241)Social Class 0.085 0.170 * 0.028

(0.055) (0.069) (0.072)Concerted Cultivation 0.320 ** 0.257 ** 0.154 ** 0.098 * 0.116 0.099

(0.074) (0.074) (0.041) (0.042) (0.062) (0.065)Interactions W/ C. CultivationBlack −0.292 * −0.268 * −0.229 * −0.211 * 0.001 0.018

(0.110) (0.110) (0.091) (0.091) (0.191) (0.191)Hispanic −0.052 −0.014 −0.014 0.011 −0.266 −0.281

(0.104) (0.105) (0.085) (0.086) (0.184) (0.186)Asian −0.242 −0.195 −0.102 −0.063 0.018 0.034

(0.214) (0.210) (0.145) (0.148) (0.157) (0.159)Other −0.210 −0.202 −0.037 −0.036 0.100 0.071

(0.162) (0.162) (0.107) (0.107) (0.229) (0.228)2nd − 3rd Grade Slope 1.607 ** 1.632 ** 1.637 ** 1.626 ** 1.662 ** 1.696 **

(0.026) (0.039) (0.014) (0.020) (0.024) (0.041)Black −0.175 ** −0.191 ** −0.140 ** −0.141 ** −0.132 * −0.145 *

(0.047) (0.048) (0.030) (0.031) (0.063) (0.064)Hispanic 0.012 0.018 −0.059 * −0.068 * 0.006 −0.012

(0.045) (0.049) (0.026) (0.028) (0.059) (0.061)Asian −0.121 −0.106 −0.176 ** −0.186 ** −0.397 ** −0.414 **

(0.095) (0.101) (0.046) (0.049) (0.051) (0.058)Other −0.185 ** −0.192 ** −0.109 ** −0.108 ** −0.123 −0.140

(0.068) (0.068) (0.039) (0.039) (0.080) (0.080)Social Class −0.018 −0.047 * −0.059 *

(0.020) (0.024) (0.024)Concerted Cultivation −0.018 −0.037 0.004 0.001 −0.021 −0.027

(0.028) (0.029) (0.014) (0.015) (0.020) (0.021)Interactions W/ C. CultivationBlack 0.000 0.009 0.018 0.020 0.008 −0.002

(0.041) (0.041) (0.033) (0.033) (0.066) (0.066)Hispanic 0.052 0.063 0.017 0.024 0.036 0.038

(0.039) (0.040) (0.030) (0.030) (0.064) (0.064)Asian 0.043 0.050 −0.004 −0.007 0.040 0.038

(0.067) (0.067) (0.044) (0.045) (0.052) (0.053)Other 0.055 0.056 −0.001 −0.005 0.025 0.039

(0.068) (0.067) (0.041) (0.041) (0.073) (0.073)

‘m’ p < .06, ‘*’p < .05, ‘**’ p < .01Note: Standard errors are in parentheses.Note 1: ‘-1’ models include second time kindergartner, age at kindergarten entry (-66 months), and whether ornot the child changed schools over the given period. ‘-2’ models include the full covariate list.

195

distribution are facing significant disadvantages for lower values of concerted culti-

vation, substantially reducing the Asian-white gap for children in lower concerted

cultivation families that are socioeconomically advantaged.

7.3 Summary

Concerted cultivation is an important predictor of children’s reading skills at

kindergarten entry, whether or not the models are group-mean centered, signif-

icantly reducing Hispanic-white gaps and statistically eliminating the black-white

gap. This is, to my knowledge, the first time a single covariate has been used to

fully explain young black and white children’s skill differences. Black and white

children continue to diverge over time, however, even when they attend the same

schools and come from families that practice the same levels of concerted culti-

vation. By the end of third grade, concerted cultivation explains nearly 30% of

the black white difference, which is sizeable, but is also disappointing given the

full mediation found at kindergarten entry. Although Hispanic-white differences

at kindergarten entry are not fully accounted for, a larger proportion of the gap

is consistently explained. By the end of third grade, accounting for concerted

cultivation reduces the Hispanic-white disparity by 49%.

As with the previous achievement tests (see Chapters 5 and 6), the pattern

for Asian children is less consistent. While children, on average, lose ground over

the summertime—although the large summertime variance indicates a significant

number of children make considerable gains, Asian children on average acquire

reading skills at an impressively large rate. Concerted cultivation is not implicated

in this summer advantage.

Social class disparities are large at kindergarten entry and in the growth rates

until the end of first grade. Concerted cultivation consistently explains approxi-

mately 20% of this gap, indicating that sizeable residual differences persist after

accounting for parents’ underlying parenting strategies. Overall, the social class

contribution is large prior to the second grade, particularly when one considers the

indirect effects through concerted cultivation.

As with general knowledge and mathematics achievement, interactions indi-

cated that black children receive fewer returns to concerted cultivation than other

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children. In looking more closely into this interaction, findings mirrored those

from the previous chapters. Lower-class black children do not receive the same

detriments associated with low levels of concerted cultivation as other children.

Although the other black by concerted cultivation were not significant for the other

social class categories, coefficients showed a marked tendency mirroring a smaller

effect size. Other researchers have reported similar findings (Farkas and Beron

2004; Sun 1998), and future research will hopefully shed more light on this partic-

ular quandary. Notably, concerted cultivation had a much stronger association for

higher social class Asian children and where the black interactions tended to be

negative in magnitude, they tended to be positive for Asian children. Sun (1998)

has previously noted a tendency toward higher returns on parental investments for

Asian children, which is consistent with these findings using the ECLS-K.

CHAPTER

EIGHT

Discussion & Conclusion

8.1 Re-Introduction

With widespread concern that the U.S. education system is underperforming in

its task to educate America’s youth, particularly for racial and ethnic minorities

and lower social class children, understanding the genesis of social group dispari-

ties is key if policy makers are to succeed in designing effective interventions. The

2001 landmark legislation commonly known as No Child Left Behind Act (NCLB),

aims to mitigate social group disparities by changing the award/reward structure

within which schools and teachers operate while increasing student-level knowl-

edge of academic failure and success through consistent, frequent assessments of

reading, mathematics, and science skills. Knowledge of student achievement is ex-

pected to provide schools and teachers with information to which they can adapt

their strategies, adjusting practices to improve efficiency and student outputs. Al-

though NCLB is a nationally mandated education policy, it allows local control.

If communities are to meet the mandates, they must be able to design educational

policies and interventions that are effective in raising student achievement.

The research presented in this dissertation is concerned with the way a series of

family practices, designated ‘concerted cultivation’ as derived from Lareau’s (2003)

Unequal Childhoods: Class, Race, and Family Life, affect student performance.

To some, the family-achievement connection may not be obvious if schooling and

family life are considered independent. For the development of children’s academic

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competencies, however, they have been found to be related, as demonstrated in

earlier chapters of this dissertation and a large body of prior research (Farkas 1996;

Farkas and Beron 2004; Reardon 2003; Downey et al. 2004; Lee and Burkam 2002;

Hart and Risley 1995; Entwisle et al. 1997; Alexander et al. 2001; Phillips et al.

1998). Since at least the Coleman Report (Coleman et al. 1966; see also Coleman

1972), research has consistently demonstrated that family background is amongst

the most important, consistent predictors of student achievement. The evidence

demonstrating large skill differences between children prior to formal schooling

(Farkas and Beron 2004; see also Lee and Burkam 2003 for a discussion of skill

differences at kindergarten entry; Phillips et al. 1998b) has meaningful implications

for policy development, although these implications have a degree of uncertainty

proportional to the limitations in our understanding of the developmental processes

that give rise to these early disparities.

Yet there is always uncertainty; but while there may be ambiguity about details,

the fact that children enter school with large achievement discrepancies across a

broad range of skills is well established (e.g., Lee and Burkam 2002). Given NCLB’s

worthwhile mandate to not only improve student performance, but also attenuate

social group disparities, researchers and policy makers would be remiss to disregard

important sources of variation in children’s lives other than schools, particularly

during the often neglected early school years (Alexander and Entwistle 1989: 1)

and earlier (Farkas and Beron 2004). Results from research research utilizing

the Early Childhood Longitudinal Study of a Kindergarten Class Cohort, 1998-

1999 (ECLS-K) suggest that schools equalize student performance in many regards

because growth rates are higher overall during the school year and variability in

growth rates over in-school periods are smaller than during the summertime, when

children are not in school (Downey et al. 2004). Disadvantaged children still grow

at slower rates during the school year; in some cases, however, these differences

persist even for children in the same schools (see Chapters 5-7; see also Reardon

2003), which implies processes leading to differentiation within schools, out-of-

school impacts, or genetic factors (e.g. Herrnstein and Murray 1994). Yet, even if

aggregate differences in growth rates between schools could be eliminated, along

with residual within-school disparities, social group discrepancies would still persist

over time due to the initial differences children enter school with.

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Given large initial differences between social groups, how are test score gaps to

be eliminated without retarding the growth of the advantaged group? There is no

easy answer to this question; rather it may be more fruitful to look to eliminating

early childhood cognitive differentials that arise prior to schooling while equalizing

mean academic growth rates and minimizing variances in these rates across schools

by minimizing inequality between schools. By studying children’s skill levels at

kindergarten entry, the research reported herein addresses the development of pre-

kindergarten skill differences. But it is also a study of children’s academic growth

through the third grade since understanding how families influence growth during

school has important implications for the growth of achievement gaps that need to

be understood if we are to speak honestly and clearly about reducing social group

disparities in academic achievement.

8.2 Discussion

8.2.1 Concerted Cultivation & Cultural Capital

Drawing upon Lareau’s (2003) ethnographic study, I have attempted to oper-

ationalize the notion of ‘concerted cultivation,’ a concept embodying different

parental orientations toward child rearing. Lareau’s research is part of a longer

tradition which has noted meaningful variation in class-based orientations towards

parenting strategies (e.g. Kohn 1977). Being heavily influenced by Pierre Bourdieu

(1977a, 1977b, 1984, 1986; Bourdieu and Passeron 1977), this work on concerted

cultivation fits into a broader conceptual matrix seeking to both elaborate the no-

tion of capital (e.g. Farkas 1996) and relate the various dimensions of capital to

status attainment processes. The key idea upon which Lareau draws in framing

her discussion of parenting strategies is the notion of cultural capital, a resource

intermediate between human and social capital in many regards. In socialization

processes, cultural capital is, on the one hand, a means of transferring human

capital from parents to offspring, and, on the other, a means of generating social

capital, connecting children to a broader network of relationships. Cultural capital

both bridges the divide between human and social capital in childhood achieve-

ment processes and has important implications for the way individuals come to see

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society and their place and roles within it.

Academic achievement is but one of many dimensions of children’s lives within

which, via parenting strategies, cultural capital is implicated. Yet the development

of cognitive skills inherent in academic achievement processes is an important di-

mension with consequences important in the development of individual biography

(Farkas 2003; Hurnstein and Murray 1994; Murnane et al. 2000; Kerckhoff et al.

2001; Raudenbush and Kasim 1998). The differing parental orientations Lareau

(2003) observed have far-reaching consequences for the organization of children’s

routines, which, in turn, have real consequences in children’s lives. These conse-

quences, which are products of parental cultural resources, have powerful impacts

on children’s life chances, setting them on paths to academic success in some cases,

and potential failure in others. Parental practices are the pathways through which

the social stratification of family life and childrearing coalesce to produce persistent

inequalities in educational experiences among and across diverse social groups.

Higher class parents, especially, engage in what Lareau termed ‘concerted cul-

tivation’ in deliberate attempts to foster their children’s cognitive and social skills.

Less advantaged parents, in contrast, engaged in a collection of practices she

termed ‘the accomplishment of natural growth.’ Both cultural repertoires rep-

resent parenting strategies that embody notions of cultural capital as reflected in

parents’ skills, habits, and styles (see Swidler 1986; Farkas 1990; 1996). Within

an intergenerational framework, it is through parenting behaviors that parents

transmit their human and social capital to their children, perpetuating advantages

and disadvantages across generations and preparing children for life as members

of particular social classes (Kohn 1977).

‘Concerted cultivation’ is a concept capturing a goal-oriented series of practices

parents adopt in order to prepare their children for immediate academic and long-

term occupational success. Higher social class parents in Lareau’s study viewed

concerted cultivation as their primary responsibility and, accordingly, these parents

structured their lives in efforts to directly stimulate their children’s development.

These families were characterized by parent-organized, multiple child leisure ac-

tivities, the use of reasoning and the elicitation of responses from children, and

interventions with institutions on their children’s behalf (Lareau 2003: 31).

Lower class parents, on the other hand, generally perceived themselves as care-

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takers, providing material resources and comforts to their children, often in the

face of financial difficulties. These parents clearly delineated the world of adults

from the world of children, and where middle and upper middle class parents prac-

ticing concerted cultivation expended great effort to structure the lives of their

children, largely relying on formal activities such as sports teams or dance lessons

where children interact repeatedly with adults, lower social class parents gave

their children much greater freedom in structuring their own activities. Corre-

spondingly, lower social class parents were much less involved in structuring their

children’s lives and were far less oriented toward managing and encouraging their

children’s skill development. For lower class children, time was structured much

more losely, language use tended to use more directives and children’s responses

were less often elicited and parents were much more dependent on institutional

actors. Whereas higher class children developed an emerging sense of entitlement,

lower class children from non-concerted cultivation practicing families developed

an emerging sense of constraint (Lareau 2003; Kohn 1977).

Lareau’s (2003) work represents an important conceptual elaboration of cultural

capital (Lamont and Lareau 1988; Bourdieu 1977, 1984; Bourdieu and Passeron

1977) through the concrete details of her observations, which enables the opera-

tionalization of behaviors and actions using survey data. Numerous studies have

attempted to directly operationalize and test notions of cultural capital, often as

high-brow culture (Dumais 2002; Aschaffenburg and Maas 1997; DeGraaf 1986;

Di Maggio 1982; Downey 1995; Ganzeboom, DeGraaf, and Robert 1990), educa-

tional resources (Teachman 1987; Blake 1981; Downey 1995; Burkam, Ready, Lee,

and Logerfo 2004; Farkas and Beron 2004), and/or skills and habits (Farkas et al.

1990; Farkas 1996). Regarding recent work attempting to operationalize concerted

cultivation, Kingston (2001: 89) argues that “(1) defined in terms of exclusionary

class-related practices and dispositions, cultural capital does not substantially ac-

count for the relationship between social privilege and academic success and (2)

too many conceptually distinct variables have come to be placed under the big

umbrella of cultural capital, creating a distorted sense of what accounts for acad-

emic success.” As Dumais (2002: 49) further notes, “there is no consensus on what

cultural capital means, whether it has an effect, or what the effect is.”

As Lareau’s (2003) work is relatively recent, no quantitative studies have yet

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attempted to operationalize cultural capital as a concept such as ‘concerted culti-

vation,’ although concerted cultivation is similar in many respects to the HOME

inventories that researchers have previously used (e.g. Farkas and Beron 2004; Guo

and Harris 200; Phillips et al. 1998a). Given the clarity of Lareau’s research, this

sort of conceptual reorientation represents a potentially important improvement

over previous research. Lareau’s (2003) work is observation-based and not simply

derived from the available covariate list in survey data. That is, there is nothing

ad hoc in the conceptualization of cultural capital as ‘concerted cultivation.’ Given

the broad range of measures researchers have utilized in attempting to operational-

ize cultural capital (e.g. Kingston 2001), concerted cultivation, with its basis in

direct observation, is a potentially unifying, theoretically justified construct. In

this vein, there is a large body of research on children’s achievement that provides

a significant amount of indirect evidence consistent with the concerted cultivation

paradigm.

8.2.2 Operationalizing ‘Concerted Cultivation’

Based on Lareau’s (2003) observations, concerted cultivation is identifiable through

a number of child-oriented activities and parental behaviors. In seeking to spon-

sor their children’s cognitive development, parents who identify with the cultural

logic of concerted cultivation expend great effort to structure their children’s time

through formal, structured leisure activities. By doing so, these parents acquire

experiences for their children that they hope will have lasting consequences for

their development through the accumulation of skills, habits, and styles, derived

from the broad experiential base to which they are exposed. Concerted cultivation

practicing parents also tend to be more comfortable and instrumentally engage

teachers and other professionals, rarely hesitating to intercede on their children’s

behalf (Lareau 2003, 1988). These parents actively search for new learning mate-

rials and speak differently to their children, engaging them in conversations and

encouraging them to rationalize and defend their own actions, sometimes to the

later irritation of parents who discover that they are raising little ‘lawyers’ who

are not always as obedient as their parents would prefer. Concerted cultivation is

a very child-oriented approach to raising children. While parents who follow the

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forms associated with the accomplishment of natural growth no doubt care equally

for their children, they allow their children to develop in a more natural, relaxed

fashion, without the continuous drive towards the development of skills.

But is concerted cultivation really identifiable in the U.S. population using sur-

vey research methods, and if so, how does this construct relate to student achieve-

ment? Below I reproduce the initial questions that guided the earlier analytic

chapters:

1. Do the parental behaviors and child activities identified by Lareau (2003),such as child activities, parental involvement with the school, and learningmaterials, covary systematically in a way consistent with the concept of ‘con-certed cultivation’?

2. Is ‘concerted cultivation’ stable over childhood? Given that concerted cul-tivation represents an underlying, often implicit strategy, the expectation isthat families should rank consistently on this measure over time.

3. How does concerted cultivation vary across population subgroups? Lareau’s(2003) observations suggest that (1) the association of concerted cultiva-tion with social class should be large, and (2) social class should be a moreimportant predictor than race. How this parenting strategy varies acrosspopulation subgroups is a question of considerable interest.

Chapter 4 attempted to answer question 1 using nonlinear factor analysis tech-

niques (i.e., IRT modeling). Although the ECLS-K does not provide measures of

parental language use (for a detailed analysis of language use, see Hart and Risley

1995), numerous items regarding parental participation with the school and the

extra-curricular activities in which the child participates are included. In addition,

information on the number of books the child has were also provided.1 These three

groups of covariates were conceptualized as each being the product of latent fac-

tors reflecting the parents’ underlying propensity to intercede with the school, the

parents’ underlying propensity to structure their children’s time through formal

activities, and a latent resources measure which was fixed to the observed indicator

(the number of books) since no other measures were available (at all time points)

to identify this factor. These latent constructs were then thought to be produced

by a higher order factor designated ‘concerted cultivation.’

1This was the only measure of material academic resources available at all three waves whenthe other measures were collected.

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With latent variable modeling, the researcher hypothesizes a covariance struc-

ture amongst measures, and then attempts to test whether or not the observed

covariance matrix fits those expectations. The approach used in this study is

analogous, although the complexity of the problem is increased because most mea-

sures used to construct the factor are binary, requiring non-linear factor analytic

techniques or IRT modeling. These methods demonstrated that the pattern of rela-

tionships was highly consistent with the expectations derived from Lareau (2003),

with the standardized loadings between the higher and lower order factors produc-

ing standardized loadings between .6 and .9, in addition to good overall model fit.2

Thus, the pattern of relationships found in the data are consistent with the notion

of ‘concerted cultivation.’

Yet one wonders if a cross-sectional covariance structure provides enough evi-

dence to justify the conclusion that concerted cultivation is a population-applicable

construct. The possibility that other processes could produce similar data struc-

tures, while not formally pursued in this work,3 at least supports the consideration

of further tests. Fortunately, the ECLS-K provides the items used to construct the

analysis three times between the kindergarten and the end of third grade. This abil-

ity to track the measurement model over time leads to question 2, which involves

the degree of temporal consistency between factor models across waves. Because

the higher-order factor thought to be concerted cultivation is expected to be a

relatively stable characteristic embodying the underlying strategies through which

parents approach child rearing, the within-period covariance structure should be

temporally consistent. This is exactly what was found, i.e., there is a high degree

of consistency between factor loadings and correlations between the higher-order

concerted cultivation constructs, which are .96 between adjacent waves and .94

between kindergarten and the end of third grade. Because different activities, for

example, may take different meanings as children age, there was a little expected

movement in the factor loadings, although this movement was generally quite small.

The high intercorrelations over a four-year span, with parents ranking consistently

2Ideally, one would like to assess model fit using the chi-squared exact-fit statistic; however,this sample—over 16,400 cases, is highly overpowered for this test—meaning that trivial differ-ences between the expected and observed covariance structures would lead to a rejection of thenull hypothesis of no difference.

3This would be a conceptual/theoretical exercise.

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in the overall distribution and the relative factorial invariance over time, strongly

suggests that the observed covariance structure between the observed indicators is

derived from the concerted cultivation latent construct.

While it may be possible to conceive of a process that would produce a single

wave covariance structure consistent with concerted cultivation, but is in fact due

to some other process, a factor consistent with the static temporal trend that is not

concerted cultivation is more difficult to conceive. As a further test, but also for de-

scriptive purposes, question 3 asks the extent to which concerted cultivation varies

across population subgroups. This last question is important both as a further

validation of the construct and because the distribution of concerted cultivation

across population subgroups is an interesting question in its own right. Lareau’s

(2003) research most strongly implicates social class, and her small sample size

makes generalizing to race and other differentiating characteristics difficult. The

results of this dissertation, however, provide very strong evidence supporting the

hypothesis of differentiation by social class and its generalizeability to the popu-

lation. Social class singly explained 48% of the variance in concerted cultivation,

logging a bivariate standardized coefficient of .7. Thus, not only did the previously

presented analyses indicate a factor structure highly consistent with Lareau’s ob-

servations of concerted cultivation, but facets of its distribution also agree. Race

contributed an additional 11% of explained variance, with very large differences

between minority group status and whites.

Other important characteristics predicting concerted cultivation were whether

or not a non-English language is spoken in the home, which had the second largest

association in the model. This finding is not surprising since concerted cultiva-

tion is a U.S.-based parenting strategy and although other cultures (such as the

Chinese, for example) may have analogues, immigrant parents are less likely to be

knowledgeable of U.S.-based parenting practices that are highly differentiated by

class. These parents are likely to enact strategies whose underlying cultural logic

is based upon the cultural logic with which they are familiar. A more nuanced

approach to cultural differences in parenting strategies for immigrant groups is a

notable avenue for future research, one that is needed in the diversifying ethnic

context of the U.S (e.g. Sun 1998).

A number of other variables were included in the models. Both step parents

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and single parents also practiced lower levels of concerted cultivation than two-

parent biological families, while part-time mothers practiced less of it than full-time

working mothers. Parents who had low educational expectations for their children,

also practiced far less than parents who expected their children to complete a high

school or college degree. Parents who enrolled their children in center-based care

prior to kindergarten also had higher concerted cultivation levels than parents who

acquired no such care for their children—although this difference is primarily the

result of different social class levels.

These findings, taken together, strongly support the generalization drawn from

Lareau (2003) that parents employ meaningfully different strategies in raising chil-

dren. What this data cannot offer is the depth that Lareau (2003) provides on the

details and meanings of these strategies. At the same time, ethnographic studies

are simply not capable of demonstrating that the phenomena under study are sim-

ply the result of a localized, non-random sample. In a recent ethnographic study,

for example, Chin and Phillips (2004) found less evidence that parents of different

social classes adopt disparate underlying cultural logics or parenting strategies. As

they note (Chin and Phillips 2004: 204):

Whether we judge parents’ values on the basis of their comments ininterviews or their behavior, most parents from all social classes be-lieved that they should actively nurture their children’s development,and most tried to do so. Yet, relative to the working-class and poorparents, the middle-class parents tended to be more successful in con-structing highly stimulating summers for their children because theytended to have greater financial resources, more-flexible jobs, and moreknowledge about how to match particular activities to their children’sskills and interests.

This argument challenges Lareau’s (2000, 2002, 2003) contentionthat middle-class families make “a deliberate and sustained effort tostimulate children’s development” (2003: 238), while working-class andpoor families view “a child’s development as unfolding spontaneously,as long as they were provided with comfort, food, shelter, and otherbasic support” (2003:238). Several explanations may account for ourdifferent results.

Chin and Phillips’ (2004) excellent study clearly documents the potential sources

of difference from Lareau’s (2003) work, noting that: (1) Lareau studied families

during the school year, whereas Chin and Phillips’ observations took place over

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the summertime. Family processes could have seasonal dependencies; (2) that

there were meaningful regional and ethnic differences between the samples; (3)

that Lareau’s study was biased toward finding class differences, whereas theirs was

biased against finding class differences. As Chin and Phillips (2004: 205) suggest,

“Lareau’s sampling strategy maximized between-class differences by selecting ‘ob-

servation’ families on the basis of their social-class backgrounds and child-rearing

practices simultaneously.” Chin and Phillips’ sampling strategy, in contrast, min-

imized between-class differences in child-rearing because children were sampled

from a single school, while maximizing within-class differences (pg. 205) “because

we deliberately sampled a wide range of children, from various ethnic, academic,

and social-class backgrounds, without considering the students’ activities and home

environments or their parents’ child-rearing practices.”

The differences between these two studies are relevant to the current application

because Chin and Phillips’ (2004) discussion highlights how variability can be built

in to a study, or omitted, by the sampling design. Lareau (2003), for example, is

clear regarding her view of gradation, which is the continuous and generally normal-

type (read ‘distributional form/assumption’) of analysis such as that used here.4 In

fact, she explicitly recommends a categorical analysis, which would be a latent class

or latent profile type of approach. However, classification becomes easier when

within-group variability decreases and/or between-group variability increases, a

point suggested by Chin and Phillips’ (2004) thoughtful discussion, particularly

as concerted cultivation might be distributed or clustered in the population. The

heterogeneity observed by Chin and Phillips (2004) provides some support, from

an ethnographic context, that the approach utilized herein has points of validity.5

However one regards this issue, the close fit of the model and its conformity to

4Note that the output factor scores in figures to are all normal in shape. There is no formalconstraint that imposes this distributional shape—it is entirely data driven, suggesting thatconcerted cultivation is in fact distributed normally in the population.

5The differences in approaches between this study and the categorical approach endorsed byLareau (2003) are epistemological in nature. Although survey research is not strictly confinedto continuous types of analysis, continuous forms are generally more easily accomplished formathematical reasons, while ethnographic research is almost necessarily categorical in nature.The goal of the ethnographic research is fundamentally to identify and classify, to recognize andrelate patterns. Survey researchers have the same goals, but differences arise as a result of differ-ences in methodologies. The close conformity between these two methodologies, despite radicallydifferent approaches and philosophical rationalizations, in the study of concerted cultivation iscompelling.

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Lareau’s (2003) expectations strongly suggests that it is reasonable at this stage

of analysis to rely on the normal-distribution approach.6 represents an interesting

avenue for future research, albeit one with perhaps less justification than is supplied

by Lareau (2003).

8.2.3 Academic Achievement

The identification of concerted cultivation using covariates available in nationally

representative data like the ECLS-K opens up the possibility for a second round

of questions—questions which provide the fundamental motivation for this disser-

tation. Concerted cultivation is a notion providing cultural insights of sociological

salience in its own right, but one of its principal uses is as a tool for understanding

sociological problems related to child development. Below are the three primary

questions motivating the analysis of children’s test scores over the early grades.

1. How is concerted cultivation related to children’s kindergarten readiness?Prior research suggest that children from families practicing concerted cul-tivation should have accrued significant advantages in school readiness bykindergarten entry.

2. Is concerted cultivation related to children’s growth over the summertime?Since schools are out of session, home environments are expected to be relatedto children’s growth over this period.

6I have attempted to estimate two different types of latent class analysis, although the resultsare preliminary (and will remain so for the time being). First, an unconstrained latent classanalysis suggests that there may be as many as seven latent classes, which is far more thanthe dichotomization Lareau (2003) suggests. The large number of classes could be a result ofincluding immigrant families in the analysis. However this remains speculative and an interestingavenue for future research. In addition, this approach is difficult to compare to the normalapproach, however, because as the number of classes increases, so does the dimensionality ofthe problem. A second approach allows a unidimensional non-parametric representation of afactor distribution to be estimated (see Aitkin 1999; Munthen and Munthen 2004). This hasthe advantage of generating a histogram which can then be compared to the results from thenormal-based analysis. However, convergence has proven to be difficult and computations timesare excessive, so I currently have no results to present even suggestively. Results are similar fora latent profile approach.

There are additional complications to the latent class approach. For one, the latent classesare not-deterministic, that is that each child belongs to all classes with some probability, so the“factor score” analogue such as that employed here in the achievement analyses is even moreproblematic (this factor score approach is less than optimal even for continuous latent variables).This would necessitate that the entire analysis be conducted in a program like Mplus, whichwould once again bring the problems discussed in section 3.2.1 to the fore, with the addition ofthe computational difficulties alluded to in the preceding paragraph.

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3. Does concerted cultivation have an impact during the school year after ad-justing for stable school and parental characteristics? If so, how strong isthis association and does it account for race and social class differences ingrowth rates?

Social group differences across the three outcomes analyzed, general knowledge,

mathematics, and reading achievement are significant and large. Disparities at

kindergarten entry are largest on the general knowledge test, which is one reason

why some researchers have opted not to use it (e.g. Fryer and Levitt 2004).7

At kindergarten entry, black and Hispanic children are expected to have general

knowledge test scores that differ from whites in the same schools by over .6 standard

deviations, while Asian children score .8 standard deviations below whites. The

social class gradient is sizeable as well, over .35 standard deviations on the test for

a standard deviation difference in social class. Adjusting for concerted cultivation

reduces the social group coefficient magnitudes by approximately 30% in all cases,

suggesting that concerted cultivation plays a decisive role in children’s general

knowledge base at kindergarten entry. After adjusting for the full covariate list,

the standardized concerted cultivation coefficient is .25, which is somewhat larger

in magnitude than the social class association.8 Although the race and social

class coefficients remain statistically significant and capture large, non-trivial social

group differences in children’s school readiness, concerted cultivation clearly plays

an important role in mediating general knowledge gaps and in predicting children’s

scores as well.

Concerted cultivation is not significantly related to children’s general knowledge

growth over the kindergarten and summer years, although higher social class chil-

dren tend to grow at faster rates over the summertime and Asian children make

significant gains over the first grade, significantly reducing the Asian-white gap

over this period. In general, children grow at relatively uniform rates after they

enter kindergarten, the small variances on the growth parameters highlighting this

fact. From the large initial differences at kindergarten entry, however, it is clear

7Interestingly, as noted previously, differential item functioning showed that the items usedto construct the general knowledge test generally functioned better across social groups thanwas the case for either reading or math. Statistically, it appears that the general knowledge testcharacteristics are better behaved than the math and reading tests, despite the large differencesin predicted scores across social groups.

8Whether the difference is statistically significant was not assessed.

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that there are large differences in growth rates prior to school, for which concerted

cultivation has strong implications.

The stories for math and reading are somewhat more complex, and to most

researchers, more meaningful since these tests comprise the main body of research

done on child achievement, and these two tests cover the fundamental and integral

components that open the doorways to higher learning. For mathematics achieve-

ment, after adjusting the equations to account for unobserved contextual varia-

tion, black children score about .34 standard deviations lower than white children

in the same schools, Hispanic children over .45 standard deviations lower, while

Asian children begin school with skills similar to whites’. Introducing concerted

cultivation reduces the black coefficient by 37% (to -.2 standard deviations) and

the Hispanic-white difference by 34% (-.3 standard deviations), indicating that a

sizeable proportion of the initial disadvantage for these two groups is the result of

differences in parenting strategies, as captured by the concerted cultivation mea-

sure. The social class gradient is also quite large, over .3 standard deviations prior

to controlling for concerted cultivation and .25 standard deviations afterwards, a

21% reduction. Concerted cultivation is an important predictor in its own right,

over .27 standard deviations initially, although the coefficient magnitude drops to

.16 standard deviations in the highly partiallized model with the full covariate list.

Results are even more dramatic for the reading achievement test. The initial

Hispanic-white within-school difference is .35 standard deviations, but is reduced

by to .21 standard deviations, a reduction of 40%, when concerted cultivation is

added to the equation. The initial difference between black and white children in

the same schools is over .2 standard deviations and is reduced to non-significance

(over 64% reduction; results are even more dramatic for the non-group-mean cen-

tered models) when concerted cultivation is added to the equation. This is the

shortest covariate list that eliminates the black-white gap that I am aware of.

The story for social class is essentially identical to that for mathematics, with

concerted cultivation implicated in an approximately 20% reduction in coefficient

magnitude. Concerted cultivation also has a sizeable relationship with children’s

reading achievement school readiness, which, in similar fashion to the mathematics

models, is slightly smaller than the social class relationship in magnitude, although

they are comparable.

211

Concerted cultivation is only modestly associated with growth after children en-

ter school, as small relationships to kindergarten and summer mathematics growth

were demonstrated, while consistent relationships with kindergarten and first grade

growth in reading were found. Despite the large adjustment to initial skills found

when controlling for concerted cultivation, reductions in growth rate differences for

math and reading were smaller, with the exception of the Hispanic-white reduc-

tions over the kindergarten and first grade years for reading, which were reduced

to nonsignificance. Overall, however, the black-white difference in both math and

reading achievement over time is dramatic. Adjusting for concerted cultivation

does have an impact, however, singly reducing the expected black-white within-

school difference at the end of third grade by 21% for mathematics and 30% for

reading. Though sizeable reductions, the black-white differences in growth are de-

rived from other sources of variation. Although the trend for the Hispanic-white

difference is to grow over the kindergarten and first grade years, concerted culti-

vation plays an important role in mediating these differences. At the end of third

grade, adjusting for concerted cultivation reduces the expected math difference by

34% and the reading disparity by nearly 50%. Although concerted cultivation does

not completely eliminate differences in growth during the kindergarten and first

grade years for mathematics, the differences in growth rates for reading reduce to

non-significance.

The patterns for Asian children are more complex. At kindergarten entry,

for both math and reading, Asian children within-schools overperform relative to

white children in families with the same levels of concerted cultivation. For reading

and mathematics, the Asian advantage grows dramatically over the summer, the

magnitude of the extra growth increasing as covariates are added to the equation.

The magnitude of these growth differences, which is approximately twice the rate

of those for other children on the math test, and are large and positive for the

reading test (nearly 4.5 times as large), during the summer when other children

lose ground, on average, are impressive and remain to be explained. Growth rates

during the school year are less consistent suggesting that more waves of data may

be needed to argue for a convincing trend, although the Asian advantage at later

ages is well documented (Sun 1998; Sue and Okazaki 1990). Over the second

and third grade, for example, Asian children grow significantly faster than white

212

children, but on average face an even larger disadvantage in reading growth rates

over this period. The frequent over-performance of Asian children when compared

with white children from families with the same levels of concerted and the ex-

treme magnitude of Asian children’s summertime advantage suggests a different

pattern of parental investment than employed by other parents (e.g. Sun 1998).

Indeed, the “Asian advantage” may, at least in part, result from differences in

social capital found in Asian communities, particularly the use of education-based

community programs (e.g. Zhou and Bankston 1998; Sun’s 1998 findings, however,

appear to contradict this). Given the imperviousness of the Asian-white differences

across model specifications, the reasons for Asian children’s advantages remain an

important area of research in need of explanation.

Another dramatic finding is a black by concerted cultivation interaction for all

three tests at kindergarten entry indicating that black children gain fewer returns

to concerted cultivation than other children. Similar findings have been reported by

Farkas and Beron (2004) and Sun (1998). This finding has important implications

since, on the one hand, the early black-white differentials are thought to be due to

environmental influences (for an alternative position, see Herrnstein and Murray

1994), yet in this case an important environmental predictor was shown to produce

a significantly smaller result for black than white children. However, a deeper

analysis highlights the point that the interactions are primarily driven by low class

black children, most of which are on the left or negative side of the concerted

cultivation distribution. The proper interpretation of this relationship is not that

black children are not benefitting as much from high levels of concerted cultivation

but that black children are not doing as poorly for low levels of concerted cultivation

as expected. This interpretation too must be handled with care, however, for

two reasons: (1) Middle-class black children showed significantly fewer returns

on concerted cultivation on the general knowledge test than other children; (2)

Although not statistically significant, middle and higher social class black children

show a marked tendency towards receiving lower returns on both the math and

reading tests.9

9Given this tendency, it is worth entertaining the notion that these effects nonsignificant as aresult of power.

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8.3 Conclusion

Children do not begin school with equal skills, nor do they learn at the same rates

during the academic year, even when they attend the same schools (see Chap-

ters 6, and 7; see also Reardon 2003). Disparities in achievement growth rates by

race and social class categorizations appear to be a persistent feature in educa-

tional processes, beginning in early childhood and continuing through adolescence

(Phillips et al. 1998b). Differences in learning rates are expected, however, due

to the variability in genetic potentialities across individuals, although how much

strictly genetic variation is expected is difficult to determine, in part because there

is no “absolute” cognitive or achievement assessment. Children’s genetic poten-

tials are not expressed in a vacuum; they are part of complex genetic-environment

interactions and the nature of these interactions represents an important area of

research (Guo and Stearns 2002). Although the ECLS-K is not a genetically-

informed data source, growth modeling techniques allowing children to have their

own growth rates suggests that variability in these rates is smaller when children

are in school than when they are out of school (Downey et al. 2004),10 supporting

the interpretation that a large proportion of the variability in children’s growth

rates is due to environmental influences. The broader between-family variability—

the inequality in family life between social groups (Lareau 2003), means that even

if growth rates between children in the same schools and between schools could be

equalized, disparities would persist due to disparities which arise before children

ever enter the formal schooling system.

The growth models presented in earlier chapters illustrate that although chil-

dren who begin school with higher skill levels do not necessarily grow faster than

their peers in the same school over the course of the school year, average growth

rates are higher in schools with higher average levels of initial achievement. While

the between-student random effect covariance structure suggests few systematic

growth differences given children’s skills at kindergarten entry, the differences in

growth rates between schools illustrates that from kindergarten entry, schools have

very different resources to draw upon: the skill levels of the students which compose

them. While the descriptive evidence does not suggest a within-school Matthew ef-

10The extent to which this is at least partially an artifact derived from a decrease in precisionresulting from a reduced fall of first grade sample size is unknown, however.

214

fect, between schools, it appears that the rich are likely to get richer. Family-based

achievement disparities appear to have consequences that transcend individuals. In

many places, parents are resources whose influences resonate harmoniously with

educators’ goals, sponsoring children’s development beneficially, while in other,

generally less advantaged areas, there persists a disjuncture between schooling and

family that represents an important piece of the inequality puzzle (Lareau 2002,

2003; Horvat et al. 2003; Lareau and Horvat 1999; Lareau and Shumar 1996).

How policy makers and educators can manage to tap the family as a resource

sponsoring child development is a difficult question, one with important implica-

tions not only for the opportunities that will become open or closed to children

as they age, but also for the success or failure of large scale policies like NCLB

and its smaller, localized constituents. Since Coleman (1981, 1982, 1985; Cole-

man, Hoffer, and Kilgore 1981) first noted the “Catholic school effect,” researchers

have debated whether or not the Catholic advantage was due to real schooling

differences or family-based selection processes (Goldberger and Cain 1982; see also

the exchange between Morgan and Sorensen 1999a,b; Carbonaro 1999; Hallinan

and Kubitschek 1999).11 This debate has moved, in recent years, to discussions

of charter schools—many of which seek very actively to involve parents in the ed-

ucation process. As with Catholic schools, parents who pursue these schools are

probably more likely to already be involved in their children’s education. How

will the public school system adapt to this changing educational environment, par-

ticularly if the most involved parents tend to be the ones to move their children

to alternatives where, on average, parents are more likely to already be involved

and where the school administrators and teachers are more responsive? In many

respects, schools are already ameliorating social group disparities (Downey et al.

2004), although differences in growth between members of disparate social groups

within the same schools persist, particularly for black children, even after a large

number of covariates are incorporated into the models.

The measure of concerted cultivation used in the analysis illustrates the role

parenting strategies play in educational processes, particularly when children are

11Sean Reardon and I, in a developing project, have found compelling evidence using propensityscore matching (Rosenbaum and Rubin 1983; Imbens 2004; Abadie, Drukker, Herr, and Imbens2004) with the ECLS-K sample that the Catholic school advantage is in fact the result of selectionprocesses, and not of schooling per se, from kindergarten through the third grade.

215

young. In fact, parental influences as captured by concerted cultivation decreased

in significance over time. Interestingly, however, there are reasons to suspect that

concerted cultivation will re-emerge as an important predictor of children’s acad-

emic achievement as they age and develop skills.12 Reading is age-graded in nature,

for example. Chall (1996 and the works cited therein; see also Chall, Jacobs, and

Baldwin 1990; Honig 1996) notes that reading proceeds in a number of stages.

Children, as the saying goes, learn to read during the kindergarten year, solidify

these skills over the second and third grade, taking the first steps in the process of

reading to learn (Snow, Burns, and Griffin 1998). Concerted cultivation is amongst

the most important predictors of children’s general knowledge achievement used

in this study with an effect size even larger than that for social class,13 suggest-

ing that after a lull in importance after the first grade, when children’s reading

skills solidify, concerted cultivation may re-emerge as the complexity of reading

tasks increases over time.14 Understanding how parenting strategies relate to chil-

dren’s changing skill-sets is an important avenue of research to be addressed in the

future.15

Concerted cultivation may also re-emerge through less directly cognitive mech-

anisms as children age. Children from concerted cultivation practicing families pre-

sumably learn self-discipline through participation in structured activities. Their

familiarity with routine, also, may have academic impacts as homework becomes

an increasingly important aspect of schooling. This does not mean that children

from concerted cultivation oriented families enjoy doing homework more than other

children, a point made by both Lareau (2003) and Chin and Phillips (2004), but

the routinization of their daily lives and focus on self-discipline could translate

into more efficient academic habits (e.g. Farkas 1996, 2003). The development of

such habits is clearly one of the primary goals of concerted cultivation. Children

from these families, Lareau (2003) noted, also demonstrate an emerging sense of

12The children of the ECLS-K will be followed through high school, so these hypotheses shouldbe be directly testable shortly.

13The statistical significance of the difference was not assessed.14My thanks to Steve Raudenbush who pointed this out in a personal communication.15The ECLS-K, in addition to the achievement assessments used for the analysis, provides

“proficiency” scores which reflect components of the tests. How concerted cultivation relates tothese dimensions will be included in future analyses. They have not been included here, however,due to the limited size of this project.

216

entitlement which could have important consequences for student-teacher interac-

tions. The notions of status socialization (Kohn 1977) captured by the concerted

cultivation paradigm suggest that these children are more likely to have a positive

self-concept, higher levels of efficacy, and a greater sense of mastery. Assessing

some of these questions will be possible in later waves of the ECLS-K when NCES

begins asking children perceptual questions that relate to these sorts of constructs.

However, by improving children’s study habits and helping them develop not only

the skills but confidence to interact with teachers and other professionals, concerted

cultivation may influence later childhood academic achievement through a diverse

set of pathways implicating processes and contexts both internal and external to

the school, along with psychological factors.

There has been some disagreement about the more general mechanisms through

which cultural capital improves student performance, with some preferring culture

‘as a signaling mechanism’ as an explanation for differences in academic compe-

tencies (Dumais 2002; Aschaffenburg and Maas 1997; DeGraaf 1986; Di Maggio

1982; Downey 1995; Ganzeboom, DeGraaf, and Robert 1990), and others pre-

ferring a more directly cognitive approach (Teachman 1987; Blake 1981; Downey

1995; Burkam, Ready, Lee, and Logerfo 2004; Farkas and Beron 2004; Farkas et al.

1990; Farkas 1996). Although the concepts alluded to in the above paragraph may

not apply directly to very young children,16 directly cognitive mechanisms appear

to play the primary role early on, although research suggests that children be-

gin forming learning-related skills prior to kindergarten (McClelland and Morrison

2002). Given that children over the initial grades are still forming and consolidat-

ing their sense of self, the role of concerted cultivation may diminish for a time

as the direct cognitive impacts that occur early in children’s lives fade into cul-

tural impacts that operate through habits and psychology. Early on, however, by

directly stimulating children through the use of learning materials, language use,

and the acquisition of stimulating experiences, cognitive stimulation is the likely

mechanism through which concerted cultivation influences children’s test scores.

Future research could address this issue by assessing the extent to which concerted

cultivation and home cognitive stimulation are related.17

16As I discuss below, however, they may—particularly for black children. See Davis (2003).17Supplementary analyses which were not shown suggest that the HOME inventory subscales

217

The possible re-emergence of concerted cultivation as an important predictor of

children’s academic competencies also suggests that race and social class disadvan-

tages may take on increased importance at later ages when children begin reading

to learn, for example. Given the very large social group differences in general

knowledge about the world demonstrated in the ECLS-K, there is little to suggest

a conclusion to the disturbing black-white test score divergence apparent at the

younger ages informing the analysis. Although black-white differences continue to

grow over time, even when models are estimated with a large covariate list, black

and white children from families with similar levels of concerted cultivation statis-

tically have the same reading scores at kindergarten entry. Though the black-white

math disparity is not fully accounted for, adjusting for parenting strategies on the

concerted cultivation continuum significantly reduces the gap. The relationship be-

tween black racial identification, concerted cultivation, and academic achievement

is complex, however, with a tendency toward lower returns to concerted cultivation

across the social class distribution, although the standard errors are generally very

large, indicating a high degree of uncertainty surrounding the mean parameter es-

timate. The only statistically consistent interaction indicated that that lower-class

black children do not have the same expected decrease in test scores that white

children do for lower levels of concerted cultivation—which is important because

many black children are disadvantaged regarding concerted cultivation. In fact,

there was little evidence in the lower social class distribution to support an in-

terpretation for expected returns to positive values of concerted cultivation being

lower; rather, the coefficient values and the distribution of concerted cultivation

distribution for this group of children suggests attenuated negative effects.

Why black by concerted cultivation interactions across social class categories

tend to be negative is difficult to answer. Future research might show, in fact, not

only statistical evidence of no differences but also 0-magnitude interaction coef-

ficients. If future research does indeed uncover significant associations, research

available in the ECLS-K load on the general concerted cultivation factor at kindergarten entry.The HOME subscales were not included in the analyses, however, for two reasons. First, thefactor loadings for the HOME subscales were not invariant over time (for the three periods used tostudy the ‘concerted cultivation’ factor), presumably because the items available in the ECLS-Kdecrease in importance as children age. Second, the measures of concerted cultivation with andwithout the HOME scale were so highly correlated that not including them had little impact onthe subsequent results. However, these results should not preclude a more systematic analysis.

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into family influences on children’s initial scores is faced with a perplexing issue.

Why might black children across the social class distribution receive fewer acad-

emic benefits to concerted cultivation than other children? Differences may reside

in the mismatch between parents and schools if black parents are less able to trans-

late actions into knowledge identified with the testing apparatus. The measure of

concerted cultivation employed assumes that concerted cultivation has the same

relationship to the outcomes across all groups, and there may be subtle devia-

tions that turn out to be important, possibly attenuating the concerted cultivation

association. Perhaps even more importantly, differences in the unobserved com-

ponents, such as verbal scaffolding may also introduce measurement error since

indicators of language use are not available in the ECLS-K, reducing the impact

of concerted cultivation although many black parents are involved with children’s

schooling, structure their children’s time through formal activities, and purchase

books for their children. In addition, the environments through which parents prac-

ticing concerted cultivation move may not be equal for black and white children,

which could possibly produce lower returns. The networks of black parents may

not provide as much information on teachers’ goals and expectations, the guiding

force behind concerted cultivation, as for white parents (e.g. Lin 2001: 95-96).

Horvat, Weininger, and Lareau (2003) found that middle-class parents were able

to more effectively draw upon contacts to glean schooling information than lower

class parents, although race differences were smaller than those for social class.18

When dealing with black-white differences in the U.S., racism is often a viable

line of pursuit, at least conceptually. However, it is not clear how racism, at least

at the classroom and school levels, could produce differential concerted cultivation

impacts when the scores at kindergarten entry operate prior to school. While

within-school processes could be important sources of black-white differences in

growth rates, once again it is hard to see how bad teachers are implicated in

children’s school readiness. Race does appear, however, to add an additional layer

of complexity to parent- and student-teacher relationships. Lareau (2002, 2003),

for example, notes that many middle-class black parents take extra steps to help

their children develop a positive sense of racial identity while also being critical

of the education system as a result of being deeply concerned with the historical

18The observations were based on 88 third and fourth grade students in three schools.

219

legacy of racial discrimination in educational arenas (Lareau and Horvat 1999).

How the development of identity relates to young children’s academic trajectories

is an important area of concern (Davis 2003), particularly if these developments

lead to an alienated stance towards education (e.g. Ogbu 1978). 19

Concerted cultivation explains large proportions of Hispanic-white gaps in test

scores both at kindergarten entry and at the close of third grade. Why Hispanic

children tend to have the same returns to concerted cultivation as white children

when black children do not is also difficult to answer, particularly when many

Hispanic children are reared in homes with an even greater linguistic mismatch

than black children. Familiarity with school English has been shown to be related

to black children’s early reading achievement (Charity, Scarborough, and Griffin

2004); however, the linguistic disparity may be reduced for Hispanic children be-

cause, while disadvantaged in English, many Hispanic children have a fluent base

in Spanish from which to draw.20 Where a Hispanic child might not know an

English word, for example, they may know the word in Spanish. If this is the case,

it is possible that language use tends to bias the concerted cultivation association

for black children downward, while the attenuation is less for Hispanic children.

Lower-class Hispanic children, as with black children, do not suffer the same nega-

tive impacts from concerted cultivation as white children on the mathematics test.

From a policy perspective, however, increasing parental knowledge and facilitating

concerted cultivation for Hispanic children is expected to produce significant gains

in Hispanic children’s test scores. Given that many Mexican immigrants, the ma-

jority of the Hispanic sample in the ECLS-K, come to the U.S. for financial reasons

and with educational aspirations, many of these parents may be keen to adopt ed-

ucationally successful parenting strategies. Because of limited finances, however,

even knowledgeable parents may not be able to fully implement the cultural logic

19Another possibility resides in the functional form of concerted cultivation. If concerted cul-tivation has differential effects at different parts of the concerted cultivation distribution (asopposed to the social class distribution, for example), the linear results could be a weightedaverage of impacts across different segments of the distribution. Given the tendency towardsnegative (non-significant) values, and uncertainty about these estimates and potential mecha-nisms, more research is in order if the views on parenting strategies put forth by Lareau (2003)are to be translated into applicable, beneficial policies which ameliorate black-white academicachievement differentials.

20It should also be noted that Hispanic children who did not pass a fluency routing test werenot administered the reading test in the ECLS-K.

220

in practice (Chin and Phillips 2004).

Concerted cultivation is less strongly implicated in the achievement processes

of Asian children. There are indications of Asian by concerted cultivation by

social class interactions, although the results tend to be test-specific. That middle

and higher social class Asian children benefit disproportionately from concerted

cultivation is a double-edged sword, implying that Asian children from families

with below average levels of concerted cultivation are doing more poorly. Overall,

however, the pattern of results is not highly consistent, and these interactions might

be the result of mixing Asian sub-populations together. Not only is the ‘Asian’

classification heterogenous, Chinese (the largest group) and Vietnamese families

bring to the U.S. educational landscape a series of education-oriented practices

which have proven successful, which may mean that immigrant and second- or

higher-generation families tend to follow different parenting strategies while only

one of these, concerted cultivation, is captured in the model. Asian children’s

tremendous growth over the summertime represents an important area of study,

and may be the product of community-based education programs which are the

direct outgrowth of the social capital accruing through local networks (Zhou and

Bankston 1998), or differential effectiveness or quality of parental investments (Sun

1998).

Just as the underlying logic of child-rearing in Asian communities may produce

social capital (Zhou and Bankston 1998), concerted cultivation may also generate

social capital. Parents mingle when dropping their daughters off at dance lessons

or while watching soccer games from the sidelines. They exchange information

about teachers and schools and how to get their children into accelerated classes

(e.g. Lareau 2003; Horvat et al. 2003). If the Asian advantage, particularly

over the summer, is the result of differences in access to social networks, it is

of a fundamentally different kind than that generated through the processes of

concerted cultivation. The extent to which concerted cultivation is not only a

form of cultural capital but also plays a role in the development of social capital

is an important and interesting area of research.21

21I have taken a preliminary look at this possibility, although I did not pursue it extensivelybecause the analysis and use of concerted cultivation as strictly cultural capital was a largetask without need of additional complications. However, there is solid evidence, I believe, thatthere is a higher-level latent generalization of concerted cultivation, although not all of the items

221

As I implicated above, the tendency to lower returns to concerted cultivation

for black middle- and upper-class black children may be a result not of concerted

cultivation as cultural capital, per se, but because of inequalities along its (yet-to-

be explored) social dimensions (see, for example, Lareau and Horvat 1999). How

this will play out as children age and the cognitive stimulatory component decreases

in importance is an open question, yet there may be real cognitive consequences

for young children if the experiences children are exposed to across environments

are more disorganized (e.g. Sampson and Groves 1989) and stressful than children

from other groups’ experiences (Massey 2004; Charles, Dinwiddie, and Massey

2004; Massey and Fischer 2002). Black children who are more likely to experience

concerted cultivation in primarily white environments may also be more likely to

experience racism (see Lareau 2003, chapter 6), a factor whose impacts could be

diverse and negative. This could, for example, be at the root of the tendency

for the race by class by concerted cultivation interactions to be negative in value

although non-significant. If some advantaged black children cross through white

environments but receive differential treatment, the coefficient estimates could be

negatively biased but also uncertain, as captured by the standard errors, due to

heterogeneity in these experiences.22 Taken together, the results presented here

suggest that family investments may have different relationships to black children’s

outcomes within different levels of the social class distribution.

Of course, concerted cultivation does not completely explain social group dis-

parities at kindergarten entry, nor differences in school year or summer learning

growth rates. As a predictor, however, it is most clearly implicated in school

readiness. How concerted cultivation operates as children age and become more

involved in seeking out their own interests remains an open and important question.

Children, as they become more autonomous, can both encourage the practices of

concerted cultivation by requesting books and activities, while they can also resist

used to specify the latent factor at the individual level are implicated. In addition, because themultilevel factor analysis is a generalization of random effect models where a higher level latentvariable predicts the random effect covariance matrix, the specter of extremely long computationtimes again intrudes, in large measure because the binary items used for the analysis result inchallenging integration problems. Readers interested in pursuing multilevel factor analysis arereferred to Munthen and Munthen (2004).

22This, presumably, would be far less a problem for the most disadvantaged black children whorarely pass through white-dominated environments, e.g. Massey and Denton (1994).

222

parents at every turn, making the practice difficult to fully realize (Lareau 2003;

Chin and Phillips 2004). Lareau (2003) also considered other costs involved in

concerted cultivation, such as the frequent trips for soccer games and the stress

of constantly organizing children’s lives. A high degree of flexibility is required

with considerable temporal and financial costs by parents who practice the highest

levels of concerted cultivation. These costs can also translate across siblings since

brothers and sisters become constrained by each others’ schedules. Thus, there

are not only many more dimensions of family life that must be studied regarding

concerted cultivation, there are also reasons to think that, globally, too much of a

good thing like concerted cultivation is not always good.

There is the possibility that the relationship between concerted cultivation and

children’s test scores is spurious and is in fact due to the genetic relatedness of

parents and offspring. This could be the case if the behaviors embodied by con-

certed cultivation have a strong genetic component and children’s test scores do

too, for instance. The ECLS-K, unfortunately, cannot address this sort of hypoth-

esis. There are suggestions, however, that the role of genetics in the concerted

cultivation and academic achievement association is relatively complex and dif-

ficult to conceptualize, at least as a main-effect. As noted above, children and

parents often disagree quite strongly about the activities of concerted cultivation.

Although there may be psycho-developmental reasons to suspect a noncorrespon-

dence between parents and children, this merely highlights the complex nature of

the phenomenon and suggests, if anything, a very muddy genetic-environmental

interaction as compared to a main effect. Parents are entirely willing to override

their children’s proclivities for their own, while children often do not hesitate to

find points of challenge. How variable these patterns of relationships are remain an

important issue for future research. In addition, parenting strategies have changed

considerably over time (see Lareau’s [2003: 245-248] discussion and the works cited

therein). If the genetic component’s main effect was large, then parenting roles

would not appear to have such a large historical and cultural component. Yet,

a minimal genetic component in the relationship remains an assumption. The

ECLS-K is not a genetically informed data source, so assessing this alternative

explanation for the concerted cultivation association is not possible with these

223

data.23

By all indications, parenting strategies, then, clearly lead to important pre-

schooling differences in children’s competencies, and until future research is able

to clarify the relationship between concerted cultivation and later test scores, policy

makers should most profitably focus on the parent-schooling relation for younger

children. At the same time, given the difficulties involved in sponsoring family-

based educational interventions, improving the quality of pre-schooling programs

like Head Start is an obvious place to begin addressing early schooling disparities.

The School Readiness Act of 2003,24 which states that Head Start should be de-

signed to provide education-oriented training to prepare children for school appears

to be a move in the right direction. Although children’s preschooling experiences

were not discussed in the analytic chapters, covariates are included and results are

presented in the Appendix tables. The results suggest that prior to The School

Readiness Act, participation in Head Start was no more beneficial to children than

receiving no care, while children in other programs fared better net of the other

control variables. Hopefully the focus on using these early care arrangements to

promote school readiness will serve to ameliorate social group disparities in chil-

dren’s cognitive skills at school entry. In order to be effective, however, greater

care will need to be taken in assessing pre-schooling programs so that successful

interventions can be modeled effectively (Anderson et al. 2003; Gilliam and Zigler

2001; Barnet 1998). Implementation and use of effective interventions in these

contexts is of key importance and should not be taken for granted.

Returning to the family-schooling connection, concerted cultivation as it is

currently embodied in U.S. society, is defined largely by social class and race/ethnic

status. As such, in order to be useful for policies that extend beyond out-of-

home care arrangements, if Lareau (2003) is correct, not only must the underlying

cultural logic of child rearing be made more independent of these factors than it

currently is, the means of realizing this logic must also be made accessible (Chin

and Phillips 2004). Chin and Phillips’ (2004) study suggests that many lower-

23It may be possible to get a handle on the question by comparing biological to adoptedchildren. The adopted children sample size is small, however, which limits power for assessingthe degree to which concerted cultivation functions disparately for these two different groups ofchildren. Nonetheless, this remains a viable line of pursuit for future research.

24http://www.acf.hhs.gov/programs/hsb/pdf/hs reauthorization.pdf

224

class and minority parents are already familiar with the underlying cultural logic

of concerted cultivation, but lack the resources to fully implement that strategy for

their children, although they may want to. Given that one of the principal goals

of NCLB is to reduce social group achievement gaps, a quote drawn from Lee and

Burkam (2002: 81) is appropriate:

As a nation, we continue to support the role—even the obligation—ofschooling to close these gaps, but at the same time we create or magnifythe same gaps with other social policies. Except for continuing supportfor Head Start (actually a relatively inexpensive program), our publicpolicies do little to address the negative educational effects that incomedisparities have on young children. The U.S. should not use one handto blame the schools for inadequately serving disadvantaged childrenwhen its social policies have helped to create these disadvantages—especially income disadvantages—with the other hand.

A lack of resources and opportunities places constraints on the feasibility for the

underlying cultural logic of concerted cultivation to be leveraged as a means of

reducing achievement disparities. There is evidence that investments in the home

cognitive environment decrease with negative changes in income (Votruba-Drzal

2003), suggesting that interventions designed to buffer the home cognitive environ-

ment from resource fluctuations could be helpful. In this vein, it is also possible

that the global concerted cultivation construct does not need to be built in to pol-

icy. Future work will do well to explore how the various dimensions of concerted

cultivation relate to children’s achievement in order to paint a clearer picture of

which child-rearing dimensions are most important for children’s cognitive devel-

opment. The cognitive resources in the home, for example, have consistently been

shown to be related to children’s achievement, although policies aimed at helping

parents leverage or gain access to these resources should prove beneficial (Farkas

and Beron 2004; Guo and Harris 2000; Phillips et al. 1998a; Sun 1998).

As a meaningful component of achievement processes, concerted cultivation is

not only a source of differentiation for children’s academic competencies, it is also

a part of larger systemic patterns of inequality that persist across generations.

Unfortunately, concerted cultivation requires not only accurate knowledge of the

underlying cultural logic upon which it is based, parents must also have the skills

and resources to actuate this logic as an organizing strategy. Yet identification of

225

this concept with nationally representative survey data illustrates the importance

of parental orientations towards their children’s educations and highlights, in a

new way, both the important role parents play in childhood achievement processes

and also the importance of macro cultural ideals in shaping children’s achievement

trajectories and, consequently, the opportunities that will be available to them

later in life.

APPENDIX

A

Appendix

Supplementary tables are provided in this appendix for each of the three achieve-

ment outcomes. Decreases in coefficient magnitude for the non-centered and group-

mean centered models are presented in tables A.1 (general knowledge), A.5 (math-

ematics), and A.9 (reading). The next set of tables presents coefficients for the

non-centered and group-mean centered growth partially-standardized with respect

to the proper variance components from the baseline growth models (i.e., the co-

efficient divided by the between-student variance of the intercept for kindergarten

entry) in tables A.2, A.6, and A.10, which are general knowledge, math, and read-

ing, respectively.

Due to the large number of analyses conducted, a number of models including

more covariates were estimated. They have not been included in the analytic

chapters to keep the focus centered on race, class, and concerted cultivation—

topics which deserved and were accorded significant attention. Many researchers

have different interests, however, so I have included in the appendix long tables

with additional regressors for those whose interests are piqued by other covariates.

The final model (G) is alluded to superficially in the analytic chapters. However,

a number of intermediate models in both non-centered and centered forms are

presented in tables A.3, A.7, and A.11, for general knowledge, mathematics, and

reading achievement, respectively.

227

A.1 Supplementary General Knowledge Tables

Table A.1. Proportion Reduction in Coefficient Magnitude Between Models for GeneralKnowledge


A-D B-E C-F C-G A-D B-E C-F C-GInitial Status

Black 0.262 0.277Hispanic 0.283 0.323Asian 0.333 0.334Other Race 0.242 0.327Social class 0.321 0.323Concerted Cultivation 0.311 0.441 0.288 0.411Second K. 0.324 0.236 0.448 0.513 0.302 0.239 0.466 0.562Age at K. Entry -0.011 0.004 0.007 0.002 -0.008 0.006 0.002 0.005

Kindergarten SlopeBlack -0.040 0.026Hispanic 0.173 0.175Asian 0.579 0.322Other Race 0.049 0.046Social class 0.048 0.035Concerted Cultivation 0.511 0.384 1.062 0.961Second K. -0.004 -0.012 0.051 0.019 0.020 0.010 0.061 0.046Changed School -0.610 -0.423 -0.017 -0.031 -0.433 -0.313 -0.024 -0.047

Summer SlopeBlack -0.170 -0.087Hispanic -0.399 -0.840Asian 1.398 1.164Other Race 0.118 -0.153Social class 0.059 0.776Concerted Cultivation 1.128 0.835 0.313 0.273Second K. 0.034 0.010 0.048 0.089 0.030 1.337 0.073 0.085Changed School -0.053 -0.020 0.007 0.015 -0.041 1.000 -0.028 -0.017

1st Grade SlopeBlack 0.650 1.088Hispanic 0.404 0.323Asian 0.172 0.154Other Race 0.146 0.422Social class 0.342 0.380Concerted Cultivation 0.378 0.421 0.301 0.368Second K. -0.243 -0.083 -0.125 -0.012 -0.276 -0.110 -0.205 -0.079Changed School 0.031 0.007 0.107 0.443 0.041 0.019 0.077 0.403

Note: Column headings refer to the proportion change for coefficients between models for tables 5.2 and 5.3.

228

Table A.2. Partially-Standardized with Respect to the Outcome Regression Coefficientsfor Table 5.2, General Knowledge

Model

A B C D E F G

Initial StatusBlack −0.780 ** −0.576 ** −0.533 ** −0.517 **Hispanic −0.732 ** −0.524 ** −0.475 ** −0.289 **Asian −0.885 ** −0.590 ** −0.675 ** −0.364 **Other Race −0.489 ** −0.371 ** −0.361 ** −0.314 **Social class 0.417 ** 0.283 ** 0.281 ** 0.222 **Concerted Cultivation 0.489 ** 0.435 ** 0.394 ** 0.337 ** 0.273 **Second K. −0.150 ** −0.104 * −0.119 * −0.101 * −0.080 −0.066 −0.058Age at K. Entry 0.060 ** 0.064 ** 0.062 ** 0.060 ** 0.064 ** 0.062 ** 0.062 **

Kindergarten SlopeBlack −0.161 * −0.167 * −0.157 * −0.171 *Hispanic −0.103 −0.085 −0.074 −0.092Asian −0.037 −0.016 −0.022 −0.074Other Race 0.036 0.034 0.038 0.031Social class 0.044 0.042 0.038 0.012Concerted Cultivation 0.036 0.028 0.024 0.018 0.022Second K. −0.282 * −0.270 * −0.286 * −0.283 * −0.273 * −0.271 * −0.280 *Changed School 0.200 0.235 0.325 * 0.322 * 0.334 * 0.330 * 0.335 *

Summer SlopeBlack 0.507 0.594 0.648 0.620Hispanic 0.221 0.310 0.325 0.441Asian −0.107 0.043 −0.055 0.133Other Race −0.649 −0.573 −0.550 −0.505Social class 0.337 * 0.317 0.346 * 0.445 *Concerted Cultivation 0.073 0.116 −0.040 −0.009 0.012Second K. −1.081 −0.948 −1.015 −1.045 −0.938 −0.966 −0.924Changed School 0.565 * 0.526 * 0.561 * 0.595 * 0.536 m 0.557 * 0.553 *

1st Grade SlopeBlack 0.143 0.050 0.030 0.061Hispanic 0.222 * 0.132 0.118 −0.008Asian 0.795 ** 0.658 ** 0.698 ** 0.493 *Other Race 0.413 ** 0.353 * 0.350 * 0.343 *Social class −0.192 ** −0.126 ** −0.143 ** −0.183 **Concerted Cultivation −0.202 ** −0.173 ** −0.162 ** −0.126 ** −0.117 *Second K. −0.125 −0.180 −0.166 −0.156 −0.195 −0.186 −0.168Changed School −0.233 −0.208 −0.230 −0.225 −0.207 −0.205 −0.128

‘m’ p < .06, ‘*’ p < .05, ‘**’ p < .01Note: Partially-Standardized with respect to the outcome regression coefficients are calculatedfrom bsy = b

sψ, where b is the regression coefficient and sψ is the between-child variance compo-

nent for the growth parameter from the null model presented in table 5.1.

229

Table A.3. Partially-Standardized with Respect to the Outcome Regression Coefficientsfor Table 5.3, General Knowledge, Group-Mean Centered

Model

A B C D E F GInitial Status

Black −0.635 ** −0.459 ** −0.422 ** −0.419 **Hispanic −0.587 ** −0.398 ** −0.351 ** −0.202 **Asian −0.800 ** −0.533 ** −0.589 ** −0.312 **Other Race −0.365 ** −0.246 ** −0.228 ** −0.196 **Social class 0.352 ** 0.238 ** 0.236 ** 0.189 **Concerted Cultivation 0.421 ** 0.375 ** 0.344 ** 0.300 ** 0.248 **Second K. −0.137 ** −0.087 −0.107 * −0.095 * −0.066 −0.057 −0.047Age at K. Entry 0.058 ** 0.061 ** 0.060 ** 0.059 ** 0.061 ** 0.060 ** 0.059 **

Kindergarten SlopeBlack −0.065 −0.063 −0.054 −0.056Hispanic −0.095 −0.078 −0.067 −0.085Asian −0.042 −0.029 −0.043 −0.099Other Race −0.076 −0.073 −0.071 −0.069Social class 0.053 0.051 0.049 0.022Concerted Cultivation 0.020 0.014 0.005 −0.001 0.001Second K. −0.265 * −0.251 * −0.262 * −0.259 * −0.248 * −0.246 * −0.250 *Changed School 0.246 0.277 m 0.354 * 0.353 * 0.364 * 0.363 * 0.371 *

Summer SlopeBlack 0.858 0.933 1.001 0.907Hispanic 0.111 0.205 0.226 0.361Asian −0.145 0.024 −0.017 0.248Other Race 0.614 0.708 0.733 0.730Social class 0.291 0.065 0.296 0.384 *Concerted Cultivation 0.140 0.179 −0.986 0.096 0.102Second K. −1.105 −1.000 −1.065 −1.072 0.337 −0.987 −0.974Changed School 0.335 0.332 0.337 0.349 0.000 0.347 0.343

1st Grade SlopeBlack 0.077 −0.007 −0.031 0.011Hispanic 0.272 * 0.184 0.169 0.051Asian 0.911 ** 0.771 ** 0.793 ** 0.578 **Other Race 0.159 0.092 0.087 0.082Social class −0.161 ** −0.100 * −0.116 * −0.152 **Concerted Cultivation −0.217 ** −0.185 ** −0.187 ** −0.151 ** −0.137 **Second K. −0.110 −0.153 −0.142 −0.141 −0.169 −0.171 −0.153Changed School −0.224 −0.195 −0.213 −0.215 −0.191 −0.197 −0.127




230

Tab

leA

.4.

Supp

lem

enta

ryM

odel

sfo

rG

ener

alK

now

ledg

eA

chie

vem

ent

wit

hth

eFu

llC

ovar

iate

Lis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

Init

ialSta

tus

22.0

58

**

22.2

57

**

22.3

92

**

21.4

16

**

20.0

37

**

20.0

38

**

20.0

41

**

20.0

39

**

(0.1

10)

(0.1

15)

(0.1

14)

(0.1

93)

(0.1

56)

(0.1

56)

(0.1

56)

(0.1

56)

Bla

ck

−3.3

99

**

−3.2

19

**

−3.0

65

**

−3.0

94

**

−2.7

36

**

−2.5

98

**

−2.4

59

**

−2.5

06

**

(0.1

88)

(0.1

93)

(0.1

92)

(0.1

93)

(0.2

26)

(0.2

29)

(0.2

28)

(0.2

29)

His

panic

−1.7

53

**

−1.7

16

**

−1.6

67

**

−1.7

31

**

−1.2

50

**

−1.2

18

**

−1.1

50

**

−1.2

06

**

(0.1

88)

(0.1

89)

(0.1

88)

(0.1

88)

(0.2

06)

(0.2

07)

(0.2

06)

(0.2

06)

Asi

an

−2.1

07

**

−2.1

26

**

−2.1

57

**

−2.1

78

**

−1.8

22

**

−1.8

40

**

−1.8

36

**

−1.8

64

**

(0.2

83)

(0.2

83)

(0.2

81)

(0.2

81)

(0.2

99)

(0.2

99)

(0.2

98)

(0.2

98)

Oth

er

−2.0

70

**

−1.9

95

**

−1.8

85

**

−1.8

77

**

−1.3

02

**

−1.2

41

**

−1.1

36

**

−1.1

70

**

(0.2

54)

(0.2

54)

(0.2

52)

(0.2

51)

(0.2

82)

(0.2

82)

(0.2

81)

(0.2

80)

Socia

lC

lass

1.6

36

**

1.5

85

**

1.4

80

**

1.3

28

**

1.3

68

**

1.3

28

**

1.2

57

**

1.1

33

**

(0.0

67)

(0.0

68)

(0.0

69)

(0.0

71)

(0.0

72)

(0.0

73)

(0.0

74)

(0.0

75)

Concert

ed

Cult

ivati

on

1.8

77

**

1.8

30

**

1.7

87

**

1.6

35

**

1.6

78

**

1.6

41

**

1.6

16

**

1.4

85

**

(0.0

72)

(0.0

73)

(0.0

72)

(0.0

73)

(0.0

75)

(0.0

76)

(0.0

76)

(0.0

77)

Age

at

K.Entr

y0.3

63

**

0.3

64

**

0.3

72

**

0.3

73

**

0.3

51

**

0.3

52

**

0.3

56

**

0.3

56

**

(0.0

11)

(0.0

11)

(0.0

11)

(0.0

10)

(0.0

11)

(0.0

11)

(0.0

11)

(0.0

11)

Fem

ale

−0.5

18

**

−0.5

04

**

−0.4

95

**

−0.4

84

**

−0.5

08

**

−0.4

97

**

−0.4

92

**

−0.4

85

**

(0.1

05)

(0.1

05)

(0.1

05)

(0.1

05)

(0.1

07)

(0.1

07)

(0.1

07)

(0.1

06)

Second

K.

−0.4

23

−0.3

96

−0.4

70

−0.3

48

−0.3

87

−0.3

62

−0.4

09

−0.2

79

(0.2

75)

(0.2

76)

(0.2

76)

(0.2

75)

(0.2

86)

(0.2

87)

(0.2

88)

(0.2

88)

Non-E

nglish

Lang.

at

Hom

e−

3.7

65

**

−3.8

98

**

−3.9

59

**

−4.0

96

**

−3.6

09

**

−3.7

18

**

−3.7

56

**

−3.8

71

**

(0.2

28)

(0.2

29)

(0.2

28)

(0.2

29)

(0.2

39)

(0.2

40)

(0.2

39)

(0.2

40)

Ste

pPare

nt

−0.7

48

**

−0.5

69

**

−0.5

55

*−

0.6

22

**

−0.4

89

*−

0.4

85

*(0

.199)

(0.2

01)

(0.2

03)

(0.2

05)

(0.2

07)

(0.2

10)

Sin

gle

Pare

nt

−0.6

35

**

−0.0

88

−0.1

04

−0.5

45

**

−0.0

59

−0.0

83

(0.1

44)

(0.1

49)

(0.1

49)

(0.1

46)

(0.1

52)

(0.1

52)

Oth

er

Fam

ily

Str

uctu

re−

0.7

69

**

−1.4

29

**

−1.3

19

**

−0.6

75

*−

1.1

80

**

−1.1

02

**

(0.2

72)

(0.2

96)

(0.2

94)

(0.2

78)

(0.3

01)

(0.2

99)

Child/A

dult

Rati

o-

1−

0.7

63

**

−0.7

14

**

−0.7

04

**

−0.6

63

**

(0.0

67)

(0.0

68)

(0.0

69)

(0.0

70)

Moth

er’

sA

ge

(cente

red)

0.0

64

**

0.0

63

**

0.0

50

**

0.0

50

**

(0.0

09)

(0.0

09)

(0.0

10)

(0.0

10)

Moth

er

Work

sPart

-Tim

e0.7

87

**

0.6

33

**

(0.1

46)

(0.1

50)

Moth

er

Doesn

’tW

ork

0.5

73

**

0.4

57

**

(0.1

44)

(0.1

49)

Moth

er

Work

ed

Pri

or

toC

.B

irth

0.1

94

0.1

39

(0.1

14)

(0.1

18)

Hig

hSchoolor

Less

Ed.

Expecta

tions

−1.4

41

**

−1.4

27

**

(0.2

05)

(0.2

06)

Gra

duate

SchoolEd.

Expecta

tions

0.3

14

*0.3

03

*(0

.133)

(0.1

34)

Hom

eB

ase

dC

are

0.7

24

**

0.6

24

**

(0.1

46)

(0.1

46)

Head

Sta

rt−

0.3

00

−0.1

73

(0.1

95)

(0.2

02)

Cente

r-B

ase

dC

are

0.7

90

**

0.6

88

**

(0.1

24)

(0.1

27)

Note

:Table

conti

nued

on

nex

tpage.

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

231

Tab

leA

.4—

Con

tinu

ed:

Supp

lem

enta

ryM

odel

sfo

rG

ener

alK

now

ledg

eA

chie

vem

ent

wit

hth

eFu

llC

ovar

iate

Lis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

Kin

derg

art

en

Slo

pe

0.8

43

**

0.8

40

**

0.8

42

**

0.8

56

**

0.8

25

**

0.8

25

**

0.8

25

**

0.8

25

**

(0.0

11)

(0.0

11)

(0.0

11)

(0.0

14)

(0.0

08)

(0.0

08)

(0.0

08)

(0.0

08)

Bla

ck

−0.0

39

*−

0.0

39

*−

0.0

36

−0.0

43

*−

0.0

11

−0.0

11

−0.0

07

−0.0

14

(0.0

19)

(0.0

19)

(0.0

19)

(0.0

20)

(0.0

23)

(0.0

24)

(0.0

24)

(0.0

24)

His

panic

−0.0

19

−0.0

19

−0.0

18

−0.0

23

−0.0

19

−0.0

19

−0.0

17

−0.0

21

(0.0

19)

(0.0

19)

(0.0

19)

(0.0

19)

(0.0

21)

(0.0

21)

(0.0

21)

(0.0

21)

Asi

an

−0.0

16

−0.0

14

−0.0

14

−0.0

18

−0.0

23

−0.0

21

−0.0

21

−0.0

25

(0.0

29)

(0.0

29)

(0.0

29)

(0.0

29)

(0.0

31)

(0.0

31)

(0.0

31)

(0.0

31)

Oth

er

0.0

09

0.0

09

0.0

10

0.0

08

−0.0

17

−0.0

17

−0.0

15

−0.0

17

(0.0

26)

(0.0

26)

(0.0

26)

(0.0

26)

(0.0

29)

(0.0

29)

(0.0

29)

(0.0

29)

Socia

lC

lass

0.0

08

0.0

09

0.0

07

0.0

03

0.0

12

0.0

12

0.0

10

0.0

06

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

08)

(0.0

08)

(0.0

08)

(0.0

08)

Concert

ed

Cult

ivati

on

0.0

06

0.0

07

0.0

06

0.0

06

0.0

02

0.0

02

0.0

02

0.0

00

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

08)

(0.0

08)

(0.0

08)

(0.0

08)

(0.0

08)

Fem

ale

−0.0

18

−0.0

18

−0.0

17

−0.0

18

−0.0

17

−0.0

17

−0.0

17

−0.0

18

(0.0

11)

(0.0

11)

(0.0

11)

(0.0

11)

(0.0

11)

(0.0

11)

(0.0

11)

(0.0

11)

Second

K.

−0.0

72

*−

0.0

71

*−

0.0

71

*−

0.0

70

*−

0.0

64

*−

0.0

64

*−

0.0

64

*−

0.0

62

*(0

.028)

(0.0

28)

(0.0

28)

(0.0

28)

(0.0

29)

(0.0

29)

(0.0

29)

(0.0

29)

Non-E

nglish

Lang.

at

Hom

e0.0

31

0.0

32

0.0

31

0.0

26

0.0

39

0.0

39

0.0

38

0.0

33

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

25)

(0.0

25)

(0.0

25)

(0.0

25)

Ste

pPare

nt

0.0

17

0.0

19

0.0

18

0.0

13

0.0

17

0.0

17

(0.0

20)

(0.0

21)

(0.0

21)

(0.0

21)

(0.0

21)

(0.0

21)

Sin

gle

Pare

nt

0.0

05

0.0

12

0.0

06

0.0

04

0.0

13

0.0

09

(0.0

15)

(0.0

15)

(0.0

15)

(0.0

15)

(0.0

16)

(0.0

16)

Oth

er

Fam

ily

Str

uctu

re−

0.0

04

−0.0

13

−0.0

13

−0.0

06

−0.0

20

−0.0

20

(0.0

28)

(0.0

30)

(0.0

30)

(0.0

29)

(0.0

31)

(0.0

31)

Child/A

dult

Rati

o-

1−

0.0

12

−0.0

09

−0.0

13

−0.0

11

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

07)

Moth

er’

sA

ge

(cente

red)

0.0

01

0.0

01

0.0

01

0.0

01

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

Changed

School

0.0

76

*0.0

78

*0.0

82

*0.0

84

*0.0

86

*0.0

88

*0.0

92

*0.0

93

*(0

.036)

(0.0

36)

(0.0

36)

(0.0

36)

(0.0

36)

(0.0

36)

(0.0

36)

(0.0

36)

Moth

er

Work

sPart

-Tim

e−

0.0

29

*−

0.0

20

(0.0

14)

(0.0

15)

Moth

er

Doesn

’tW

ork

−0.0

19

−0.0

13

(0.0

13)

(0.0

13)

Hig

hSchoolor

Less

Ed.

Expecta

tions

−0.0

34

−0.0

44

*(0

.021)

(0.0

21)

Gra

duate

SchoolEd.

Expecta

tions

0. 0

19

0.0

18

(0.0

13)

(0.0

14)

Note

:Table

conti

nued

on

nex

tpage.

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

232

Tab

leA

.4—

Con

tinu

ed:

Supp

lem

enta

ryM

odel

sfo

rG

ener

alK

now

ledg

eA

chie

vem

ent

wit

hth

eFu

llC

ovar

iate

Lis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

Sum

mer

Slo

pe

0.4

04

**

0.3

86

*0.3

88

**

0.3

79

**

0.4

28

**

0.4

28

**

0.4

28

**

0.4

28

**

(0.0

43)

(0.0

45)

(0.0

45)

(0.0

50)

(0.0

30)

(0.0

30)

(0.0

30)

(0.0

30)

Bla

ck

0.1

06

0.0

94

0.0

88

0.1

00

0.1

55

0.1

41

0.1

36

0.1

46

(0.0

76)

(0.0

77)

(0.0

77)

(0.0

78)

(0.0

93)

(0.0

94)

(0.0

94)

(0.0

94)

His

panic

0.0

64

0.0

63

0.0

59

0.0

71

0.0

53

0.0

51

0.0

49

0.0

58

(0.0

75)

(0.0

75)

(0.0

75)

(0.0

75)

(0.0

84)

(0.0

84)

(0.0

84)

(0.0

84)

Asi

an

0.0

12

0.0

14

0.0

14

0.0

21

0.0

34

0.0

38

0.0

38

0.0

40

(0.1

16)

(0.1

16)

(0.1

16)

(0.1

16)

(0.1

23)

(0.1

23)

(0.1

23)

(0.1

23)

Oth

er

−0.0

85

−0.0

87

−0.0

88

−0.0

81

0.1

18

0.1

13

0.1

13

0.1

18

(0.0

93)

(0.0

94)

(0.0

94)

(0.0

94)

(0.1

09)

(0.1

09)

(0.1

09)

(0.1

10)

Socia

lC

lass

0.0

53

m0.0

59

*0.0

63

*0.0

72

*0.0

45

0.0

51

0.0

55

0.0

62

*(0

.028)

(0.0

28)

(0.0

28)

(0.0

28)

(0.0

30)

(0.0

30)

(0.0

31)

(0.0

31)

Concert

ed

Cult

ivati

on

−0.0

05

0.0

00

0.0

01

0.0

02

0.0

12

0.0

17

0.0

17

0.0

16

(0.0

30)

(0.0

30)

(0.0

30)

(0.0

30)

(0.0

31)

(0.0

31)

(0.0

31)

(0.0

32)

Fem

ale

0.0

14

0.0

12

0.0

14

0.0

16

0.0

11

0.0

09

0.0

10

0.0

12

(0.0

43)

(0.0

43)

(0.0

43)

(0.0

43)

(0.0

44)

(0.0

44)

(0.0

44)

(0.0

44)

Second

K.

−0.1

53

−0.1

52

−0.1

54

−0.1

49

−0.1

59

−0.1

59

−0.1

61

−0.1

57

(0.1

08)

(0.1

08)

(0.1

08)

(0.1

09)

(0.1

13)

(0.1

14)

(0.1

14)

(0.1

14)

Non-E

nglish

Lang.

at

Hom

e−

0.0

31

−0.0

17

−0.0

13

0.0

00

−0.0

77

−0.0

62

−0.0

58

−0.0

51

(0.0

90)

(0.0

90)

(0.0

90)

(0.0

91)

(0.0

95)

(0.0

95)

(0.0

95)

(0.0

96)

Ste

pPare

nt

0.0

73

0.0

53

0.0

55

0.0

71

0.0

54

0.0

55

(0.0

65)

(0.0

66)

(0.0

66)

(0.0

67)

(0.0

67)

(0.0

67)

Sin

gle

Pare

nt

0.0

62

0.0

42

0.0

45

0.0

74

0.0

51

0.0

54

(0.0

47)

(0.0

50)

(0.0

51)

(0.0

49)

(0.0

51)

(0.0

52)

Oth

er

Fam

ily

Str

uctu

re0.0

08

0.0

45

0.0

54

0.0

40

0.0

70

0.0

78

(0.0

94)

(0.0

99)

(0.0

99)

(0.0

97)

(0.1

02)

(0.1

03)

Child/A

dult

Rati

o-

10.0

13

0.0

13

0.0

22

0.0

22

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

25)

Moth

er’

sA

ge

(cente

red)

−0.0

03

−0.0

03

−0.0

02

−0.0

02

(0.0

04)

(0.0

04)

(0.0

04)

(0.0

04)

Changed

School

0.0

85

m0.0

90

*0.0

91

*0.0

89

*0.0

51

0.0

54

0.0

56

0.0

55

(0.0

45)

(0.0

45)

(0.0

45)

(0.0

44)

(0.0

51)

(0.0

51)

(0.0

51)

(0.0

51)

Moth

er

Work

sPart

-Tim

e0.0

43

0.0

44

(0.0

38)

(0.0

39)

Moth

er

Doesn

’tW

ork

0.0

12

0.0

18

(0.0

36)

(0.0

37)

Hig

hSchoolor

Less

Ed.

Expecta

tions

0.0

34

0.0

38

(0.0

63)

(0.0

63)

Gra

duate

SchoolEd.

Expecta

tions

−0. 0

58

−0. 0

35

(0.0

38)

(0.0

39)

Note

:Table

conti

nued

on

nex

tpage.

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

233

Tab

leA

.4—

Con

tinu

ed:

Supp

lem

enta

ryM

odel

sfo

rG

ener

alK

now

ledg

eA

chie

vem

ent

wit

hth

eFu

llC

ovar

iate

Lis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

1s

tG

rade

Slo

pe

0.6

57

**

0.6

64

**

0.6

67

**

0.6

87

**

0.6

62

**

0.6

62

**

0.6

62

**

0.6

62

**

(0.0

14)

(0.0

15)

(0.0

15)

(0.0

16)

(0.0

10)

(0.0

10)

(0.0

10)

(0.0

10)

Bla

ck

0.0

11

0.0

19

0.0

20

0.0

12

0.0

01

0.0

08

0.0

09

0.0

02

(0.0

24)

(0.0

25)

(0.0

25)

(0.0

25)

(0.0

30)

(0.0

30)

(0.0

30)

(0.0

30)

His

panic

0.0

04

0.0

05

**

0.0

04

−0.0

02

0.0

15

0.0

16

0.0

14

0.0

10

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

27)

(0.0

27)

(0.0

27)

(0.0

27)

Asi

an

0.1

04

**

0.1

04

**

0.1

05

**

0.0

97

**

0.1

19

**

0.1

19

**

0.1

19

**

0.1

14

**

(0.0

36)

(0.0

36)

(0.0

36)

(0.0

36)

(0.0

39)

(0.0

39)

(0.0

39)

(0.0

39)

Oth

er

0.0

67

*0.0

70

*0.0

70

*0.0

68

*0.0

17

0.0

19

0.0

19

0.0

16

(0.0

30)

(0.0

30)

(0.0

31)

(0.0

31)

(0.0

35)

(0.0

35)

(0.0

35)

(0.0

35)

Socia

lC

lass

−0.0

28

**

−0.0

30

**

−0.0

29

**

−0.0

36

**

−0.0

23

*−

0.0

25

*−

0.0

23

*−

0.0

30

**

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

10)

(0.0

10)

(0.0

10)

(0.0

10)

Concert

ed

Cult

ivati

on

−0.0

19

*−

0.0

20

*−

0.0

20

*−

0.0

23

*−

0.0

23

*−

0.0

25

*−

0.0

25

*−

0.0

27

**

(0.0

09)

(0.0

10)

(0.0

10)

(0.0

10)

(0.0

10)

(0.0

10)

(0.0

10)

(0.0

10)

Fem

ale

−0.0

29

*−

0.0

28

*−

0.0

29

*−

0.0

30

*−

0.0

29

*−

0.0

28

*−

0.0

28

*−

0.0

30

*(0

.014)

(0.0

14)

(0.0

14)

(0.0

14)

(0.0

14)

(0.0

14)

(0.0

14)

(0.0

14)

Second

K.

−0.0

41

−0.0

40

−0.0

36

−0.0

33

−0.0

37

−0.0

36

−0.0

33

−0.0

30

(0.0

35)

(0.0

35)

(0.0

35)

(0.0

35)

(0.0

36)

(0.0

36)

(0.0

36)

(0.0

36)

Non-E

nglish

Lang.

at

Hom

e0.0

72

*0.0

66

**

0.0

67

*0.0

60

*0.0

88

0.0

82

**

0.0

83

**

0.0

77

*(0

.028)

(0.0

28)

(0.0

28)

(0.0

28)

(0.0

29)

(0.0

29)

(0.0

29)

(0.0

29)

Ste

pPare

nt

−0.0

12

−0.0

15

−0.0

18

−0.0

12

−0.0

15

−0.0

17

(0.0

19)

(0.0

19)

(0.0

19)

(0.0

20)

(0.0

20)

(0.0

20)

Sin

gle

Pare

nt

−0.0

29

**

−0.0

22

−0.0

28

−0.0

27

−0.0

20

−0.0

25

(0.0

14)

(0.0

15)

(0.0

16)

(0.0

15)

(0.0

16)

(0.0

16)

Oth

er

Fam

ily

Str

uctu

re−

0.0

29

−0.0

13

−0.0

08

−0.0

41

−0.0

26

−0.0

22

(0.0

30)

(0.0

31)

(0.0

32)

(0.0

32)

(0.0

33)

(0.0

33)

Child/A

dult

Rati

o-

1−

0.0

13

−0.0

10

−0.0

12

−0.0

10

(0.0

07)

(0.0

07)

(0.0

08)

(0.0

08)

Moth

er’

sA

ge

(cente

red)

−0.0

02

−0.0

02

−0.0

02

−0.0

01

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

Changed

School

−0.0

39

−0.0

35

−0.0

32

−0.0

25

−0.0

37

−0.0

34

−0.0

31

−0.0

25

(0.0

35)

(0.0

35)

(0.0

36)

(0.0

36)

(0.0

36)

(0.0

36)

Moth

er

Work

sPart

-Tim

e−

0.0

27

*−

0.0

23

*(0

.035)

(0.0

35)

(0.0

11)

(0.0

11)

Moth

er

Doesn

’tW

ork

−0.0

31

**

−0.0

28

*(0

.011)

(0.0

11)

Hig

hSchoolor

Less

Ed.

Expecta

tions

−0.0

64

**

−0.0

60

**

(0.0

14)

(0.0

15)

Gra

duate

SchoolEd.

Expecta

tions

0.0

23

*0.0

24

*(0

.010)

(0.0

10)

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

234

A.2 Supplementary Math Achievement Tables

Table A.5. Proportion Reduction in Coefficient Magnitude Between Models for MathAchievement



Black 0.423 0.372Hispanic 0.390 0.335Asian 45.046 -3.996Other Race 0.299 0.464Social class 0.233 0.213Concerted Cultivation 0.361 0.480 0.314 0.428Second K. 0.269 0.200 0.366 0.472 0.187 0.119 0.303 0.423Age at K. Entry 0.007 0.019 0.006 -0.001 0.005 0.009 0.001 0.005

Kindergarten SlopeBlack 0.172 0.148Hispanic 0.342 0.354Asian 1.167 2.246Other Race 0.241 0.196Social class 0.237 0.191Concerted Cultivation 0.434 0.482 0.372 0.396Second K. 0.065 0.026 0.108 0.129 0.066 0.029 0.121 0.134Changed School 0.401 0.306 0.098 0.259 0.461 0.375 0.205 0.432

Summer SlopeBlack 0.832 0.586Hispanic 4.396 -1.882Asian -0.211 -0.181Other Race 0.616 6.405Social class 0.243 0.432Concerted Cultivation 0.110 0.064 -0.101 -0.119Second K. 0.127 0.070 0.115 -0.003 0.133 0.060 0.049 -0.067Changed School -0.059 -0.065 0.164 0.054 -0.029 -0.030 0.034 0.016

1st Grade SlopeBlack 0.136 0.109Hispanic 0.270 0.200Asian 0.173 0.144Other Race 0.086 0.094Social class 0.270 0.162Concerted Cultivation 0.554 0.566 0.606 0.590Second K. 0.045 0.026 0.057 0.092 0.036 0.019 0.103 0.123Changed School 0.043 0.015 0.149 0.335 0.028 0.005 0.114 0.282

2nd − 3rd Grade SlopeBlack 0.033 0.020Hispanic 0.393 0.504Asian -0.088 -0.046Other Race 0.268 -0.076Social class -0.059 -0.067Concerted Cultivation 1.443 1.411 2.058 1.854Second K. 0.010 -0.005 0.078 0.104 0.004 -0.006 0.062 0.075Changed School 0.075 0.025 0.154 0.184 0.061 0.009 0.046 -0.004


235

Table A.6. Partially-Standardized with Respect to the Outcome Regression Coefficientsfor Table 6.2, Mathematics Achievement

Model

A B C D E F G

Initial StatusBlack −0.454 ** −0.262 ** −0.183 ** −0.145 **Hispanic −0.554 ** −0.338 ** −0.250 ** −0.215 **Asian −0.006 0.250 ** 0.192 ** 0.229 **Other Race −0.305 ** −0.214 ** −0.196 ** −0.162 **Social class 0.403 ** 0.309 ** 0.286 ** 0.233 **Concerted Cultivation 0.347 ** 0.324 ** 0.235 ** 0.222 ** 0.181 **Second K. −0.179 ** −0.142 ** −0.155 ** −0.131 ** −0.114 * −0.098 * −0.082Age at K. Entry 0.067 ** 0.070 ** 0.068 ** 0.066 ** 0.069 ** 0.067 ** 0.068 **

Kindergarten SlopeBlack −0.424 ** −0.351 ** −0.320 ** −0.313 **Hispanic −0.239 ** −0.157 ** −0.126 ** −0.101 *Asian −0.088 0.015 −0.007 0.036Other Race −0.162 ** −0.123 * −0.110 * −0.097Social class 0.154 ** 0.118 ** 0.103 ** 0.083 **Concerted Cultivation 0.143 ** 0.121 ** 0.097 ** 0.081 ** 0.074 **Second K. −0.223 ** −0.200 ** −0.215 ** −0.208 ** −0.194 ** −0.192 ** −0.187 **Changed School −0.160 −0.149 −0.105 −0.096 −0.103 −0.095 −0.078

Summer SlopeBlack −0.044 −0.007 0.006 0.007Hispanic −0.009 0.030 0.044 0.025Asian 0.244 * 0.296 ** 0.291 ** 0.261 *Other Race −0.030 −0.012 −0.016 −0.019Social class 0.069 ** 0.052 * 0.046 0.047Concerted Cultivation 0.046 * 0.055 * 0.030 0.040 0.043Second K. −0.072 −0.063 −0.066 −0.063 −0.059 −0.058 −0.066Changed School 0.087 0.070 0.091 0.092 * 0.075 0.076 0.086

1st Grade SlopeBlack −0.341 ** −0.294 ** −0.275 ** −0.257 **Hispanic −0.192 ** −0.140 ** −0.119 ** −0.131 *Asian −0.350 ** −0.290 ** −0.306 ** −0.322 **Other Race −0.242 ** −0.221 ** −0.208 ** −0.194 **Social class 0.096 ** 0.070 ** 0.066 ** 0.043 *Concerted Cultivation 0.102 ** 0.073 ** 0.072 ** 0.045 ** 0.044 *Second K. −0.195 * −0.181 * −0.188 * −0.186 * −0.177 * −0.177 * −0.170 *Changed School −0.137 −0.114 −0.132 −0.131 −0.112 −0.112 −0.088

2nd − 3rd Grade SlopeBlack −0.296 ** −0.286 ** −0.261 ** −0.238 **Hispanic −0.029 −0.018 0.009 −0.013Asian 0.190 ** 0.207 ** 0.190 ** 0.178 **Other Race −0.009 −0.007 −0.001 0.014Social class 0.091 ** 0.097 ** 0.085 ** 0.064 **Concerted Cultivation 0.024 * 0.021 −0.011 −0.011 −0.010Second K. −0.241 ** −0.228 ** −0.246 ** −0.239 ** −0.230 ** −0.226 ** −0.220 **Changed School −0.029 −0.038 −0.035 −0.026 −0.037 −0.029 −0.028


sψ, where b is the regression coefficient and sψ is the between-child variance

component for the growth parameter from the null model presented in table 6.1.

236

Table A.7. Partially-Standardized with Respect to the Outcome Regression Coefficientsfor Table 6.3, Mathematics Achievement, Group-Mean Centered

Model


Black −0.341 ** −0.214 ** −0.169 ** −0.143 **Hispanic −0.453 ** −0.301 ** −0.248 ** −0.210 **Asian 0.047 0.234 ** 0.181 ** 0.224 **Other Race −0.171 ** −0.092 −0.071 −0.050Social class 0.312 ** 0.246 ** 0.229 ** 0.192 **Concerted Cultivation 0.273 ** 0.260 ** 0.193 ** 0.187 ** 0.156 **Second K. −0.194 ** −0.150 ** −0.174 ** −0.158 ** −0.132 * −0.122 * −0.101 *Age at K. Entry 0.067 ** 0.068 ** 0.067 ** 0.067 ** 0.068 ** 0.067 ** 0.067 **

Kindergarten SlopeBlack −0.362 ** −0.309 ** −0.286 ** −0.281 **Hispanic −0.183 ** −0.118 ** −0.093 * −0.078Asian −0.039 0.048 0.023 0.051Other Race −0.180 ** −0.145 * −0.134 * −0.124 *Social class 0.150 ** 0.121 ** 0.112 ** 0.093 **Concerted Cultivation 0.123 ** 0.113 ** 0.084 ** 0.077 ** 0.074 **Second K. −0.210 ** −0.181 ** −0.199 ** −0.196 ** −0.175 ** −0.175 ** −0.172 **Changed School −0.127 −0.106 −0.076 −0.068 −0.066 −0.060 −0.043

Summer SlopeBlack −0.046 −0.019 −0.017 −0.018Hispanic 0.016 0.045 0.048 0.042Asian 0.252 * 0.297 ** 0.298 ** 0.280 *Other Race −0.003 0.018 0.018 0.020Social class 0.033 0.019 0.014 0.017Concerted Cultivation 0.049 * 0.058 * 0.043 0.054 * 0.055 *Second K. −0.056 −0.048 −0.048 −0.048 −0.045 −0.046 −0.052Changed School 0.145 ** 0.137 * 0.148 ** 0.149 ** 0.141 * 0.143 ** 0.145 **

1st Grade SlopeBlack −0.248 ** −0.221 ** −0.206 ** −0.189 *Hispanic −0.175 ** −0.140 * −0.121 * −0.124 *Asian −0.287 ** −0.245 ** −0.265 ** −0.272 **Other Race −0.157 * −0.143 −0.133 −0.122Social class 0.098 ** 0.082 ** 0.080 ** 0.061 **Concerted Cultivation 0.072 ** 0.054 ** 0.045 * 0.028 0.030Second K. −0.167 * −0.153 * −0.165 * −0.161 * −0.150 m −0.148 m −0.145Changed School −0.112 −0.086 −0.104 −0.109 −0.086 −0.092 −0.075

2nd − 3rd Grade SlopeBlack −0.269 ** −0.264 ** −0.250 ** −0.231 **Hispanic −0.012 −0.006 0.011 −0.005Asian 0.184 ** 0.192 ** 0.177 ** 0.164 **Other Race 0.042 0.045 0.052 0.061Social class 0.075 ** 0.080 ** 0.073 ** 0.055 **Concerted Cultivation 0.011 0.011 −0.015 −0.012 −0.009Second K. −0.255 ** −0.239 ** −0.256 ** −0.254 ** −0.240 ** −0.240 ** −0.237 **Changed School −0.034 −0.036 −0.035 −0.032 −0.036 −0.033 −0.035


sψ, where b is the regression coefficient and sψ is the between-child variance

component for the growth parameter from the null model presented in table 6.1.

237

Tab

leA

.8.

Supp

lem

enta

ryM

odel

sfo

rM

athe

mat

ics

Ach

ieve

men

tw

ith

the

Full

Cov

aria

teLis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

Init

ialSta

tus

19.0

90

**

19.3

28

**

19.4

13

**

18.0

65

**

18.0

83

**

18.0

85

**

18.0

87

**

18.0

83

**

(0.1

38)

(0.1

45)

(0.1

44)

(0.2

75)

(0.1

53)

(0.1

53)

(0.1

53)

(0.1

53)

Bla

ck

−1.3

58

**

−1.0

64

**

−0.9

16

**

−1.0

47

**

−1.2

73

**

−1.0

39

**

−0.9

37

**

−1.0

34

**

(0.2

50)

(0.2

57)

(0.2

56)

(0.2

56)

(0.3

07)

(0.3

11)

(0.3

12)

(0.3

13)

His

panic

−1.5

71

**

−1.5

09

**

−1.4

43

**

−1.5

54

**

−1.5

79

**

−1.5

18

**

−1.4

53

**

−1.5

18

**

(0.2

40)

(0.2

39)

(0.2

39)

(0.2

39)

(0.2

69)

(0.2

69)

(0.2

69)

(0.2

69)

Asi

an

1.7

33

**

1.7

68

**

1.7

33

**

1.6

59

**

1.6

72

**

1.7

00

**

1.6

99

**

1.6

23

**

(0.3

58)

(0.3

57)

(0.3

57)

(0.3

57)

(0.3

84)

(0.3

83)

(0.3

83)

(0.3

84)

Oth

er

−1.4

01

**

−1.2

69

**

−1.1

67

**

−1.1

75

**

−0.5

07

−0.4

01

−0.3

20

−0.3

64

(0.3

28)

(0.3

28)

(0.3

27)

(0.3

24)

(0.3

71)

(0.3

71)

(0.3

71)

(0.3

70)

Socia

lC

lass

2.0

51

**

1.9

89

**

1.8

81

**

1.6

85

**

1.6

38

**

1.5

88

**

1.5

26

**

1.3

86

**

(0.0

87)

(0.0

88)

(0.0

90)

(0.0

92)

(0.0

95)

(0.0

96)

(0.0

96)

(0.0

98)

Concert

ed

Cult

ivati

on

1.5

77

**

1.5

22

**

1.4

83

**

1.3

06

**

1.3

30

**

1.2

87

**

1.2

69

**

1.1

27

**

(0.0

91)

(0.0

91)

(0.0

91)

(0.0

92)

(0.0

96)

(0.0

97)

(0.0

97)

(0.0

98)

Age

at

K.Entr

y0.4

84

**

0.4

86

**

0.4

92

**

0.4

89

**

0.4

83

**

0.4

85

**

0.4

87

**

0.4

83

**

(0.0

16)

(0.0

16)

(0.0

16)

(0.0

16)

(0.0

17)

(0.0

17)

(0.0

17)

(0.0

16)

Fem

ale

−0.1

72

−0.1

51

−0.1

52

−0.1

37

−0.1

79

−0.1

62

−0.1

65

−0.1

57

(0.1

36)

(0.1

36)

(0.1

36)

(0.1

36)

(0.1

38)

(0.1

38)

(0.1

38)

(0.1

38)

Second

K.

−0.7

24

*−

0.6

65

−0.7

54

*−

0.5

92

−0.8

96

*−

0.8

38

*−

0.8

87

*−

0.7

28

*(0

.355)

(0.3

56)

(0.3

55)

(0.3

54)

(0.3

72)

(0.3

73)

(0.3

73)

(0.3

72)

Non-E

nglish

Lang.

at

Hom

e−

0.6

56

*−

0.8

41

**

−0.9

16

**

−1.1

54

**

−0.7

65

**

−0.9

16

**

−0.9

54

**

−1.1

08

**

(0.2

67)

(0.2

68)

(0.2

68)

(0.2

71)

(0.2

85)

(0.2

87)

(0.2

87)

(0.2

90)

Ste

pPare

nt

−0.7

98

**

−0.5

51

*−

0.5

49

*−

0.5

35

m−

0.3

84

−0.3

96

(0.2

73)

(0.2

76)

(0.2

75)

(0.2

77)

(0.2

80)

(0.2

79)

Sin

gle

Pare

nt

−0.8

17

**

−0.3

41

−0.3

66

−0.7

20

**

−0.3

54

−0.3

85

m(0

.186)

(0.1

94)

(0.1

96)

(0.1

91)

(0.1

98)

(0.2

02)

Oth

er

Fam

ily

Str

uctu

re−

1.5

76

**

−2.4

58

**

−2.3

98

**

−1.3

86

**

−1.9

42

**

−1.9

24

**

(0.3

67)

(0.3

99)

(0.3

99)

(0.3

73)

(0.4

05)

(0.4

05)

Child/A

dult

Rati

o-

1−

0.5

97

**

−0.5

51

**

−0.4

90

**

−0.4

53

**

(0.0

89)

(0.0

91)

(0.0

92)

(0.0

94)

Moth

er’

sA

ge

(cente

red)

0.0

77

**

0.0

74

**

0.0

50

**

0.0

49

**

(0.0

12)

(0.0

12)

(0.0

12)

(0.0

13)

Moth

er

Work

sPart

-Tim

e0.7

04

**

0.5

54

**

(0.1

87)

(0.1

89)

Moth

er

Doesn

’tW

ork

0.6

09

**

0.4

82

*(0

.195)

(0.1

99)

Moth

er

Work

ed

Pri

or

toC

.B

irth

−0.0

06

0.0

53

(0.1

73)

(0.1

76)

Hig

hSchoolor

Less

Ed.

Expecta

tions

−0.9

90

**

−0.8

50

**

(0.2

56)

(0.2

58)

Gra

duate

SchoolEd.

Expecta

tions

0.8

28

**

0.6

81

**

(0.1

58)

(0.1

63)

Hom

eB

ase

dC

are

0.7

04

**

0.5

82

**

(0.2

14)

(0.2

18)

Head

Sta

rt0.0

40

0.2

07

(0.2

74)

(0.2

88)

Cente

r-B

ase

dC

are

1.5

17

**

1.2

61

**

(0.1

84)

(0.1

90)

Note

:Table

conti

nued

on

nex

tpage.

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

238

Tab

leA

.8—

Con

tinu

ed:

Supp

lem

enta

ryM

odel

sfo

rM

athe

mat

ics

Ach

ieve

men

tw

ith

the

Full

Cov

aria

teLis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

Kin

derg

art

en

Slo

pe

1.7

57

**

1.7

66

**

1.7

64

**

1.7

84

**

1.6

56

**

1.6

56

**

1.6

56

**

1.6

56

**

(0.0

19)

(0.0

20)

(0.0

20)

(0.0

23)

(0.0

14)

(0.0

14)

(0.0

14)

(0.0

14)

Bla

ck

−0.2

48

**

−0.2

31

**

−0.2

31

**

−0.2

43

**

−0.2

20

**

−0.2

06

**

−0.2

06

**

−0.2

18

**

(0.0

33)

(0.0

34)

(0.0

34)

(0.0

34)

(0.0

40)

(0.0

40)

(0.0

40)

(0.0

41)

His

panic

−0.0

75

*−

0.0

71

*−

0.0

69

*−

0.0

78

*−

0.0

60

−0.0

56

−0.0

53

−0.0

60

(0.0

31)

(0.0

31)

(0.0

31)

(0.0

31)

(0.0

35)

(0.0

35)

(0.0

35)

(0.0

35)

Asi

an

0.0

30

0.0

34

0.0

36

0.0

28

0.0

40

0.0

45

0.0

46

0.0

40

(0.0

47)

(0.0

47)

(0.0

47)

(0.0

47)

(0.0

50)

(0.0

50)

(0.0

50)

(0.0

50)

Oth

er

−0.0

81

−0.0

73

−0.0

72

−0.0

75

−0.1

02

*−

0.0

94

m−

0.0

93

m−

0.0

96

*(0

.043)

(0.0

43)

(0.0

43)

(0.0

43)

(0.0

49)

(0.0

49)

(0.0

49)

(0.0

49)

Socia

lC

lass

0.0

77

**

0.0

75

**

0.0

74

**

0.0

64

**

0.0

85

**

0.0

83

**

0.0

81

**

0.0

72

**

(0.0

11)

(0.0

12)

(0.0

12)

(0.0

12)

(0.0

12)

(0.0

12)

(0.0

13)

(0.0

13)

Concert

ed

Cult

ivati

on

0.0

66

**

0.0

64

**

0.0

64

**

0.0

58

**

0.0

66

**

0.0

64

**

0.0

63

**

0.0

58

**

(0.0

12)

(0.0

12)

(0.0

12)

(0.0

12)

(0.0

13)

(0.0

13)

(0.0

13)

(0.0

13)

Fem

ale

−0. 0

79

**

−0. 0

77

**

−0. 0

77

**

−0. 0

78

**

−0. 0

77

**

−0. 0

76

**

−0. 0

76

**

−0. 0

77

**

(0.0

18)

(0.0

18)

(0.0

18)

(0.0

18)

(0.0

18)

(0.0

18)

(0.0

18)

(0.0

18)

Second

K.

−0.1

59

**

−0.1

53

**

−0.1

54

**

−0.1

45

**

−0.1

47

**

−0.1

41

**

−0.1

43

**

−0.1

34

**

(0.0

46)

(0.0

46)

(0.0

46)

(0.0

46)

(0.0

47)

(0.0

47)

(0.0

47)

(0.0

47)

Non-E

nglish

Lang.

at

Hom

e−

0.0

53

−0.0

62

−0.0

61

−0.0

73

*−

0.0

28

−0.0

35

−0.0

35

−0.0

45

(0.0

35)

(0.0

35)

(0.0

35)

(0.0

36)

(0.0

37)

(0.0

37)

(0.0

37)

(0.0

38)

Ste

pPare

nt

−0.0

22

−0.0

16

−0.0

17

−0.0

22

−0.0

13

−0.0

15

(0.0

34)

(0.0

35)

(0.0

35)

(0.0

35)

(0.0

36)

(0.0

36)

Sin

gle

Pare

nt

−0.0

29

−0.0

30

−0.0

35

−0.0

21

−0.0

21

−0.0

26

(0.0

24)

(0.0

25)

(0.0

26)

(0.0

25)

(0.0

26)

(0.0

26)

Oth

er

Fam

ily

Str

uctu

re−

0.1

36

**

−0.1

52

**

−0.1

44

**

−0.1

28

**

−0.1

54

**

−0.1

48

**

(0.0

47)

(0.0

51)

(0.0

51)

(0.0

48)

(0.0

52)

(0.0

52)

Child/A

dult

Rati

o-

10.0

06

0.0

10

0.0

06

0.0

10

(0.0

12)

(0.0

12)

(0.0

12)

(0.0

12)

Moth

er’

sA

ge

(cente

red)

0.0

01

0.0

01

0.0

02

0.0

02

(0.0

02)

(0.0

02)

(0.0

02)

(0.0

02)

Changed

School

−0.0

76

−0.0

69

−0.0

67

−0.0

60

−0.0

47

−0.0

42

−0.0

40

−0.0

33

(0.0

73)

(0.0

73)

(0.0

73)

(0.0

73)

(0.0

73)

(0.0

73)

(0.0

73)

(0.0

73)

Moth

er

Work

sPart

-Tim

e−

0.0

04

−0.0

02

(0.0

23)

(0.0

24)

Moth

er

Doesn

’tW

ork

−0.0

22

−0.0

22

(0.0

21)

(0.0

22)

Hig

hSchoolor

Less

Ed.

Expecta

tions

−0.1

41

**

−0.1

40

**

(0.0

34)

(0.0

35)

Gra

duate

SchoolEd.

Expecta

tions

0.0

16

0.0

18

Note

:Table

conti

nued

on

nex

tpage.

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

239

Tab

leA

.8—

Con

tinu

ed:

Supp

lem

enta

ryM

odel

sfo

rM

athe

mat

ics

Ach

ieve

men

tw

ith

the

Full

Cov

aria

teLis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

Sum

mer

Slo

pe

0.4

73

**

0.4

56

**

0.4

50

**

0.3

97

**

0.5

34

**

0.5

34

**

0.5

34

**

0.5

34

**

(0.0

81)

(0.0

85)

(0.0

85)

(0.0

95)

(0.0

55)

(0.0

55)

(0.0

55)

(0.0

55)

Bla

ck

0.0

19

−0.0

03

0.0

02

0.0

14

−0.0

28

−0.0

52

−0.0

49

−0.0

38

(0.1

42)

(0.1

45)

(0.1

45)

(0.1

46)

(0.1

75)

(0.1

77)

(0.1

77)

(0.1

78)

His

panic

0.0

47

0.0

45

0.0

48

0.0

53

0.0

81

0.0

77

0.0

82

0.0

88

(0.1

42)

(0.1

42)

(0.1

42)

(0.1

43)

(0.1

61)

(0.1

61)

(0.1

61)

(0.1

61)

Asi

an

0.5

43

*0.5

43

*0.5

44

*0.5

49

*0.5

81

*0.5

85

*0.5

90

*0.5

89

*(0

.218)

(0.2

18)

(0.2

18)

(0.2

18)

(0.2

33)

(0.2

33)

(0.2

33)

(0.2

34)

Oth

er

−0.0

38

−0.0

47

−0.0

43

−0.0

39

0.0

42

0.0

34

0.0

42

0.0

42

(0.1

79)

(0.1

79)

(0.1

80)

(0.1

80)

(0.2

11)

(0.2

11)

(0.2

11)

(0.2

12)

Socia

lC

lass

0.0

97

m0.1

03

*0.0

95

0.0

99

0.0

28

0.0

36

0.0

31

0.0

35

(0.0

51)

(0.0

52)

(0.0

53)

(0.0

53)

(0.0

57)

(0.0

57)

(0.0

58)

(0.0

58)

Concert

ed

Cult

ivati

on

0.0

94

0.0

99

0.0

97

0.0

90

0.1

17

*0.1

24

*0.1

23

*0.1

15

*(0

.054)

(0.0

54)

(0.0

54)

(0.0

55)

(0.0

57)

(0.0

58)

(0.0

58)

(0.0

58)

Fem

ale

0.0

07

0.0

04

0.0

03

0.0

07

0.0

27

0.0

24

0.0

22

0.0

27

(0.0

80)

(0.0

80)

(0.0

80)

(0.0

80)

(0.0

82)

(0.0

82)

(0.0

82)

(0.0

82)

Second

K.

−0.1

23

−0.1

31

−0.1

37

−0.1

38

−0.0

95

−0.1

02

−0.1

06

−0.1

08

(0.2

08)

(0.2

08)

(0.2

08)

(0.2

08)

(0.2

19)

(0.2

19)

(0.2

20)

(0.2

20)

Non-E

nglish

Lang.

at

Hom

e0.1

56

0.1

70

0.1

65

0.1

73

0.1

16

0.1

36

0.1

32

0.1

49

(0.1

61)

(0.1

63)

(0.1

63)

(0.1

63)

(0.1

73)

(0.1

74)

(0.1

74)

(0.1

75)

Ste

pPare

nt

0.0

18

0.0

48

0.0

45

0.0

83

0.1

06

0.1

01

(0.1

33)

(0.1

34)

(0.1

35)

(0.1

38)

(0.1

39)

(0.1

40)

Sin

gle

Pare

nt

0.0

73

0.0

71

0.0

80

0.0

99

0.0

86

0.0

88

(0.0

96)

(0.1

01)

(0.1

02)

(0.0

99)

(0.1

04)

(0.1

05)

Oth

er

Fam

ily

Str

uctu

re0.0

98

0.0

13

0.0

38

0.1

09

0.0

36

0.0

59

(0.1

87)

(0.1

99)

(0.1

99)

(0.1

91)

(0.2

02)

(0.2

03)

Child/A

dult

Rati

o-

10.0

21

0.0

21

0.0

32

0.0

35

(0.0

46)

(0.0

47)

(0.0

48)

(0.0

48)

Moth

er’

sA

ge

(cente

red)

0.0

07

0.0

07

0.0

06

0.0

05

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

07)

Changed

School

0.1

61

0.1

72

0.1

75

0.1

80

0.2

98

*0.3

03

**

0.3

08

**

0.3

05

*(0

.098)

(0.0

98)

(0.0

98)

(0.0

98)

(0.1

13)

(0.1

13)

(0.1

13)

(0.1

13)

Moth

er

Work

sPart

-Tim

e0.1

68

*0.1

57

*(0

.077)

(0.0

78)

Moth

er

Doesn

’tW

ork

0.0

27

−0.0

19

(0.0

75)

(0.0

77)

Hig

hSchoolor

Less

Ed.

Expecta

tions

0.0

12

0.0

34

(0.1

13)

(0.1

13)

Gra

duate

SchoolEd.

Expecta

tions

−0. 0

11

−0. 0

29

(0.0

66)

(0.0

67)

Note

:Table

conti

nued

on

nex

tpage.

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

240

Tab

leA

.8—

Con

tinu

ed:

Supp

lem

enta

ryM

odel

sfo

rM

athe

mat

ics

Ach

ieve

men

tw

ith

the

Full

Cov

aria

teLis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

1s

tG

rade

Slo

pe

2.5

48

**

2.5

65

**

2.5

65

**

2.5

98

**

2.4

00

**

2.4

00

**

2.4

00

**

2.4

01

**

(0.0

30)

(0.0

32)

(0.0

32)

(0.0

34)

(0.0

22)

(0.0

22)

(0.0

22)

(0.0

22)

Bla

ck

−0.2

63

**

−0.2

46

**

−0.2

45

**

−0.2

57

**

−0.1

96

**

−0.1

82

**

−0.1

81

**

−0.1

89

**

(0.0

53)

(0.0

54)

(0.0

54)

(0.0

54)

(0.0

64)

(0.0

65)

(0.0

65)

(0.0

65)

His

panic

−0.1

27

*−

0.1

22

*−

0.1

21

*−

0.1

31

*−

0.1

25

*−

0.1

20

*−

0.1

18

*−

0.1

24

*(0

.051)

(0.0

51)

(0.0

51)

(0.0

52)

(0.0

57)

(0.0

58)

(0.0

58)

(0.0

58)

Asi

an

−0.3

14

**

−0.3

10

**

−0.3

12

**

−0.3

23

**

−0.2

67

**

−0.2

64

**

−0.2

65

**

−0.2

73

**

(0.0

77)

(0.0

77)

(0.0

77)

(0.0

78)

(0.0

83)

(0.0

83)

(0.0

83)

(0.0

83)

Oth

er

−0.2

04

**

−0.1

93

**

−0.1

92

**

−0.1

94

*−

0.1

30

−0.1

21

−0.1

21

−0.1

22

(0.0

68)

(0.0

68)

(0.0

68)

(0.0

68)

(0.0

78)

(0.0

78)

(0.0

78)

(0.0

78)

Socia

lC

lass

0.0

63

**

0.0

59

**

0.0

57

**

0.0

43

*0.0

79

**

0.0

75

**

0.0

74

**

0.0

61

**

(0.0

19)

(0.0

19)

(0.0

19)

(0.0

19)

(0.0

20)

(0.0

21)

(0.0

21)

(0.0

21)

Concert

ed

Cult

ivati

on

0.0

58

**

0.0

54

**

0.0

54

**

0.0

44

*0.0

42

*0.0

38

0.0

38

0.0

30

(0.0

19)

(0.0

20)

(0.0

20)

(0.0

20)

(0.0

21)

(0.0

21)

(0.0

21)

(0.0

21)

Fem

ale

−0.1

22

**

−0.1

20

**

−0.1

19

**

−0.1

25

**

−0.1

25

**

−0.1

23

**

−0.1

22

**

−0.1

28

**

(0.0

29)

(0.0

29)

(0.0

29)

(0.0

29)

(0.0

30)

(0.0

30)

(0.0

30)

(0.0

30)

Second

K.

−0.1

93

*−

0.1

84

*−

0.1

86

*−

0.1

71

*−

0.1

65

*−

0.1

58

*−

0.1

59

*−

0.1

45

(0.0

76)

(0.0

76)

(0.0

76)

(0.0

76)

(0.0

78)

(0.0

78)

(0.0

78)

(0.0

78)

Non-E

nglish

Lang.

at

Hom

e0.0

38

0.0

22

0.0

21

0.0

00

0.0

29

0.0

14

0.0

14

−0.0

05

(0.0

56)

(0.0

57)

(0.0

57)

(0.0

57)

(0.0

61)

(0.0

61)

(0.0

61)

(0.0

61)

Ste

pPare

nt

−0.0

63

−0.0

61

−0.0

56

−0.0

74

−0.0

72

−0.0

69

(0.0

44)

(0.0

44)

(0.0

45)

(0.0

46)

(0.0

46)

(0.0

46)

Sin

gle

Pare

nt

−0.0

39

−0.0

33

−0.0

33

−0.0

34

−0.0

25

−0.0

24

(0.0

33)

(0.0

35)

(0.0

35)

(0.0

33)

(0.0

35)

(0.0

36)

Oth

er

Fam

ily

Str

uctu

re−

0.1

80

**

−0.1

87

**

−0.1

78

*−

0.1

65

*−

0.1

68

*−

0.1

62

*(0

.067)

(0.0

70)

(0.0

70)

(0.0

68)

(0.0

71)

(0.0

71)

Child/A

dult

Rati

o-

1−

0.0

07

−0.0

03

−0.0

13

−0.0

10

(0.0

17)

(0.0

17)

(0.0

17)

(0.0

17)

Moth

er’

sA

ge

(cente

red)

0.0

01

0.0

01

0.0

01

0.0

01

(0.0

02)

(0.0

02)

(0.0

02)

(0.0

02)

Changed

School

−0.1

07

−0.0

94

−0.0

97

−0.0

88

−0.0

88

−0.0

78

−0.0

81

−0.0

75

(0.0

82)

(0.0

82)

(0.0

82)

(0.0

82)

(0.0

84)

(0.0

84)

(0.0

84)

(0.0

84)

Moth

er

Work

sPart

-Tim

e−

0.0

25

−0.0

31

(0.0

25)

(0.0

26)

Moth

er

Doesn

’tW

ork

−0.0

09

−0.0

03

(0.0

24)

(0.0

25)

Hig

hSchoolor

Less

Ed.

Expecta

tions

−0.2

25

**

−0.2

14

**

(0.0

34)

(0.0

34)

Gra

duate

SchoolEd.

Expecta

tions

0.0

25

0.0

27

(0.0

22)

(0.0

23)

Note

:Table

conti

nued

on

nex

tpage.

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

241

Tab

leA

.8—

Con

tinu

ed:

Supp

lem

enta

ryM

odel

sfo

rM

athe

mat

ics

Ach

ieve

men

tw

ith

the

Full

Cov

aria

teLis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

2n

d−

3r

dG

rade

Slo

pe

1.2

36

**

1.2

41

**

1.2

44

**

1.2

51

**

1.1

93

**

1.1

93

**

1.1

93

**

1.1

93

**

(0.0

09)

(0.0

10)

(0.0

10)

(0.0

11)

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

07)

Bla

ck

−0.1

03

**

−0.0

93

**

−0.0

93

**

−0.0

99

**

−0.0

98

**

−0.0

91

**

−0.0

91

**

−0.0

96

**

(0.0

17)

(0.0

17)

(0.0

17)

(0.0

17)

(0.0

20)

(0.0

20)

(0.0

20)

(0.0

20)

His

panic

0.0

02

0.0

04

0.0

02

−0.0

05

0.0

03

0.0

05

0.0

02

−0.0

02

(0.0

16)

(0.0

16)

(0.0

16)

(0.0

16)

(0.0

18)

(0.0

18)

(0.0

18)

(0.0

18)

Asi

an

0.0

78

**

0.0

80

**

0.0

80

**

0.0

74

**

0.0

72

*0.0

74

**

0.0

73

**

0.0

68

**

(0.0

23)

(0.0

23)

(0.0

23)

(0.0

23)

(0.0

25)

(0.0

25)

(0.0

25)

(0.0

25)

Oth

er

0.0

02

0.0

05

0.0

05

0.0

06

0.0

24

0.0

26

0.0

26

0.0

25

(0.0

22)

(0.0

22)

(0.0

22)

(0.0

22)

(0.0

25)

(0.0

25)

(0.0

25)

(0.0

25)

Socia

lC

lass

0.0

33

**

0.0

32

**

0.0

34

**

0.0

27

**

0.0

29

**

0.0

28

**

0.0

29

**

0.0

23

**

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

Concert

ed

Cult

ivati

on

0.0

02

0.0

02

0.0

02

−0.0

04

0.0

02

0.0

02

0.0

02

−0.0

04

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

Fem

ale

−0.0

64

**

−0.0

63

**

−0.0

63

**

−0.0

66

**

−0.0

61

**

−0.0

60

**

−0.0

60

**

−0.0

63

**

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

Second

K.

−0.1

02

**

−0.1

01

**

−0.0

98

**

−0.0

91

**

−0.1

08

**

−0.1

07

**

−0.1

05

**

−0.0

98

**

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

24)

Non-E

nglish

Lang.

at

Hom

e0.0

17

0.0

12

0.0

14

0.0

03

0.0

18

0.0

15

0.0

16

0.0

07

(0.0

17)

(0.0

18)

(0.0

18)

(0.0

18)

(0.0

19)

(0.0

19)

(0.0

19)

(0.0

19)

Ste

pPare

nt

0.0

12

0.0

07

0.0

08

0.0

13

0.0

08

0.0

08

(0.0

15)

(0.0

15)

(0.0

15)

(0.0

15)

(0.0

15)

(0.0

15)

Sin

gle

Pare

nt

−0.0

29

*−

0.0

23

m−

0.0

21

−0.0

25

*−

0.0

21

−0.0

20

(0.0

11)

(0.0

12)

(0.0

12)

(0.0

12)

(0.0

12)

(0.0

13)

Oth

er

Fam

ily

Str

uctu

re−

0.0

41

−0.0

26

−0.0

13

−0.0

32

−0.0

16

−0.0

05

(0.0

23)

(0.0

24)

(0.0

24)

(0.0

23)

(0.0

24)

(0.0

24)

Child/A

dult

Rati

o-

1−

0.0

12

−0.0

09

−0.0

09

−0.0

07

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

Moth

er’

sA

ge

(cente

red)

−0.0

02

*−

0.0

02

*−

0.0

02

*−

0.0

02

*(0

.001)

(0.0

01)

(0.0

01)

(0.0

01)

Changed

School

−0.0

13

−0.0

13

−0.0

13

−0.0

12

−0.0

15

−0.0

15

−0.0

15

−0.0

15

(0.0

11)

(0.0

11)

(0.0

11)

(0.0

11)

(0.0

12)

(0.0

12)

(0.0

12)

(0.0

12)

Moth

er

Work

sPart

-Tim

e0.0

11

0.0

10

(0.0

10)

(0.0

11)

Moth

er

Doesn

’tW

ork

−0.0

03

−0.0

01

(0.0

10)

(0.0

10)

Hig

hSchoolor

Less

Ed.

Expecta

tions

−0.1

16

**

−0.1

10

**

(0.0

15)

(0.0

16)

Gra

duate

SchoolEd.

Expecta

tions

0.0

26

*0.0

27

*(0

.011)

(0.0

11)

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

242

A.3 Supplementary Reading Achievement Tables

Table A.9. Proportion Reduction in Coefficient Magnitude Between Models for ReadingAchievement



Black 0.796 0.638Hispanic 0.436 0.400Asian -2.102 -1.462Other Race 0.462 0.767Social class 0.223 0.209Concerted Cultivation 0.296 0.459 0.266 0.433Second K. -0.368 -0.159 -0.272 -0.405 -0.283 -0.103 -0.290 -0.481Age at K. Entry 0.013 0.037 -0.031 -0.101 0.012 0.021 -0.016 -0.062

Kindergarten SlopeBlack 0.190 0.178Hispanic 0.565 0.526Asian -0.632 -0.597Other Race 0.557 0.395Social class 0.157 0.133Concerted Cultivation 0.470 0.739 0.399 0.665Second K. 0.030 0.007 0.071 0.152 0.035 0.011 0.074 0.158Changed School 0.243 0.159 0.023 0.173 0.272 0.182 0.047 0.219

Summer SlopeBlack -2.047 -0.340Hispanic -0.823 -0.419Asian -0.196 -0.144Other Race -1.869 -0.124Social class 0.064 0.044Concerted Cultivation 0.289 0.601 0.164 0.525Second K. 0.086 -0.008 0.113 0.231 0.075 0.009 0.170 0.326Changed School 0.155 0.095 -0.293 -0.045 40.096 0.683 1.298 1.075

1st Grade SlopeBlack 0.217 0.245Hispanic 0.288 0.413Asian 0.691 1.831Other Race 0.261 0.661Social class 0.241 0.220Concerted Cultivation 0.370 0.535 0.317 0.502Second K. 0.042 0.023 0.073 0.169 0.029 0.011 0.052 0.147Changed School 0.011 -0.021 0.121 0.241 0.014 -0.013 0.093 0.195

2nd − 3rd Grade SlopeBlack 0.052 0.035Hispanic 0.197 0.323Asian 0.036 0.029Other Race 0.010 0.007Social class 1.303 -29.538Concerted Cultivation 0.693 1.050 0.473 0.924Second K. 0.039 0.066 -0.003 0.136 0.022 0.036 -0.036 0.066Changed School 0.154 0.171 0.205 0.399 0.110 0.120 0.068 0.193


243

Table A.10. Partially-Standardized with Respect to the Outcome Regression Coeffi-cients for Table 7.2, Reading Achievement

Model

A B C D E F G

Initial StatusBlack −0.239 ** −0.049 0.026 0.041Hispanic −0.419 ** −0.236 ** −0.166 ** −0.134 **Asian 0.118 * 0.365 ** 0.303 ** 0.346 **Other Race −0.176 ** −0.095 * −0.087 m −0.060Social class 0.373 ** 0.290 ** 0.276 ** 0.233 **Concerted Cultivation 0.319 ** 0.323 ** 0.214 ** 0.224 ** 0.173 **Second K. 0.141 ** 0.185 ** 0.177 ** 0.193 ** 0.214 ** 0.225 ** 0.248 **Age at K. Entry 0.035 ** 0.038 ** 0.035 ** 0.034 ** 0.036 ** 0.036 ** 0.038 **

Kindergarten SlopeBlack −0.275 ** −0.223 ** −0.194 ** −0.171 **Hispanic −0.126 ** −0.055 −0.022 −0.025Asian 0.127 * 0.207 ** 0.183 ** 0.177 **Other Race −0.056 −0.025 −0.011 −0.001Social class 0.147 ** 0.123 ** 0.110 ** 0.093 **Concerted Cultivation 0.103 ** 0.095 ** 0.057 ** 0.054 ** 0.027Second K. −0.356 ** −0.331 ** −0.352 ** −0.345 ** −0.328 ** −0.327 ** −0.298 **Changed School −0.215 * −0.207 * −0.170 −0.163 −0.174 −0.166 −0.141

Summer SlopeBlack 0.026 0.080 0.109 0.115Hispanic 0.061 0.111 0.136 0.145Asian 0.372 ** 0.444 ** 0.419 ** 0.414 **Other Race 0.011 0.031 0.032 0.026Social class 0.123 ** 0.115 ** 0.113 ** 0.113 **Concerted Cultivation 0.058 * 0.084 ** 0.014 0.041 0.023Second K. −0.130 −0.097 −0.116 −0.119 −0.098 −0.103 −0.089Changed School −0.054 −0.086 −0.054 −0.046 −0.078 −0.069 −0.056

1st Grade SlopeBlack −0.311 ** −0.244 ** −0.216 ** −0.213 **Hispanic −0.279 ** −0.198 ** −0.169 ** −0.170 **Asian −0.142 * −0.044 −0.067 −0.061Other Race −0.118 * −0.087 −0.079 −0.069Social class 0.153 ** 0.116 ** 0.106 ** 0.084 **Concerted Cultivation 0.145 ** 0.128 ** 0.106 ** 0.092 ** 0.068 **Second K. −0.372 ** −0.354 ** −0.366 ** −0.356 ** −0.346 ** −0.339 ** −0.304 **Changed School −0.308 ** −0.271 ** −0.311 ** −0.305 ** −0.277 ** −0.273 ** −0.236 **

2nd − 3rd Grade SlopeBlack −0.262 ** −0.248 ** −0.246 ** −0.257 **Hispanic −0.080 * −0.064 * −0.063 m −0.078 *Asian −0.401 ** −0.387 ** −0.385 ** −0.397 **Other Race −0.219 ** −0.217 ** −0.219 ** −0.218 **Social class 0.014 −0.004 −0.003 −0.024Concerted Cultivation 0.045 ** 0.014 0.046 ** 0.014 −0.002Second K. −0.079 −0.084 −0.077 −0.076 −0.078 −0.078 −0.067Changed School −0.052 * −0.073 ** −0.059 * −0.044 −0.061 * −0.047 m −0.035




244

Table A.11. Partially-Standardized with Respect to the Outcome Regression Coeffi-cients for Table 7.3, Reading Achievement, Group-Mean Centered

Model


Black −0.204 ** −0.074 −0.030 −0.020Hispanic −0.350 ** −0.210 ** −0.161 ** −0.132 **Asian 0.130 * 0.320 ** 0.269 ** 0.310 **Other Race −0.105 * −0.024 −0.005 0.011Social class 0.296 ** 0.234 ** 0.222 ** 0.192 **Concerted Cultivation 0.261 ** 0.262 ** 0.185 ** 0.192 ** 0.148 **Second K. 0.135 * 0.181 ** 0.161 ** 0.173 ** 0.200 ** 0.207 ** 0.238 **Age at K. Entry 0.036 ** 0.037 ** 0.036 ** 0.035 ** 0.036 ** 0.036 ** 0.038 **

Kindergarten SlopeBlack −0.253 ** −0.208 ** −0.185 ** −0.166 **Hispanic −0.114 ** −0.054 −0.026 −0.030Asian 0.123 * 0.197 ** 0.170 ** 0.156 *Other Race −0.075 −0.046 −0.035 −0.025Social class 0.147 ** 0.127 ** 0.118 ** 0.101 **Concerted Cultivation 0.097 ** 0.096 ** 0.056 ** 0.058 ** 0.032 *Second K. −0.358 ** −0.327 ** −0.349 ** −0.346 ** −0.324 ** −0.323 ** −0.294 **Changed School −0.180 −0.164 −0.136 −0.131 −0.134 −0.129 −0.106

Summer SlopeBlack 0.108 0.145 0.166 0.155Hispanic 0.090 0.128 0.147 0.159Asian 0.391 ** 0.448 ** 0.430 ** 0.433 **Other Race 0.181 0.203 0.211 0.203Social class 0.108 ** 0.104 ** 0.102 ** 0.105 **Concerted Cultivation 0.047 0.070 * 0.014 0.039 0.022Second K. −0.112 −0.082 −0.102 −0.104 −0.082 −0.084 −0.069Changed School 0.000 −0.011 0.008 0.008 −0.003 −0.002 −0.001

1st Grade SlopeBlack −0.193 ** −0.146 ** −0.130 * −0.123 *Hispanic −0.147 ** −0.086 −0.064 −0.079Asian −0.040 0.033 0.012 −0.010Other Race −0.045 −0.015 −0.004 0.003Social class 0.118 ** 0.092 ** 0.087 ** 0.068 **Concerted Cultivation 0.105 ** 0.100 ** 0.075 ** 0.072 ** 0.053 **Second K. −0.371 ** −0.352 ** −0.365 ** −0.360 ** −0.348 ** −0.346 ** −0.311 **Changed School −0.277 ** −0.240 ** −0.272 ** −0.273 ** −0.243 ** −0.247 ** −0.219 *

2nd − 3rd Grade SlopeBlack −0.193 ** −0.186 ** −0.187 ** −0.199 **Hispanic −0.027 −0.018 −0.019 −0.025Asian −0.346 ** −0.336 ** −0.334 ** −0.341 **Other Race −0.127 * −0.127 * −0.127 * −0.130 *Social class 0.000 −0.012 −0.007 −0.025Concerted Cultivation 0.031 * 0.014 0.036 * 0.016 0.002Second K. −0.089 −0.090 −0.085 −0.087 −0.087 −0.088 −0.079Changed School −0.079 ** −0.088 ** −0.077 ** −0.070 * −0.077 ** −0.071 ** −0.062 *




245

Tab

leA

.12.

Supp

lem

enta

ryM

odel

sfo

rR

eadi

ngA

chie

vem

ent

wit

hth

eFu

llC

ovar

iate

Lis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

Init

ialSta

tus

22.9

66

**

23.1

70

**

23.3

76

**

21.6

86

**

23.2

20

**

23.2

22

**

23.2

25

**

23.2

20

**

(0.1

60)

(0.1

68)

(0.1

68)

(0.3

19)

(0.1

62)

(0.1

62)

(0.1

62)

(0.1

62)

Bla

ck

0.0

91

0.2

70

0.4

86

0.3

50

−0.3

92

−0.2

40

−0.0

86

−0.1

69

(0.2

93)

(0.3

00)

(0.2

99)

(0.3

01)

(0.3

65)

(0.3

69)

(0.3

70)

(0.3

73)

His

panic

−1.1

13

**

−1.0

73

**

−1.0

39

**

−1.1

31

**

−1.1

46

**

−1.1

09

**

−1.0

70

**

−1.1

13

**

(0.2

82)

(0.2

82)

(0.2

81)

(0.2

80)

(0.3

16)

(0.3

16)

(0.3

16)

(0.3

16)

Asi

an

3.0

59

**

3.0

50

**

3.0

05

**

2.9

29

**

2.7

19

**

2.7

05

**

2.6

99

**

2.6

25

**

(0.4

26)

(0.4

26)

(0.4

25)

(0.4

25)

(0.4

58)

(0.4

58)

(0.4

57)

(0.4

58)

Oth

er

−0.7

25

m−

0.6

47

−0.5

08

−0.5

09

−0.0

49

0.0

18

0.1

27

0.0

91

(0.3

80)

(0.3

81)

(0.3

79)

(0.3

76)

(0.4

33)

(0.4

34)

(0.4

33)

(0.4

32)

Socia

lC

lass

2.3

48

**

2.2

93

**

2.1

90

**

1.9

72

**

1.8

86

**

1.8

38

**

1.7

84

**

1.6

26

**

(0.1

03)

(0.1

04)

(0.1

05)

(0.1

08)

(0.1

12)

(0.1

13)

(0.1

14)

(0.1

16)

Concert

ed

Cult

ivati

on

1.7

48

**

1.7

00

**

1.6

47

**

1.4

59

**

1.4

66

**

1.4

26

**

1.4

00

**

1.2

52

**

(0.1

10)

(0.1

11)

(0.1

11)

(0.1

13)

(0.1

17)

(0.1

18)

(0.1

18)

(0.1

19)

Age

at

K.Entr

y0.3

13

**

0.3

15

**

0.3

26

**

0.3

25

**

0.3

17

**

0.3

17

**

0.3

24

**

0.3

19

**

(0.0

19)

(0.0

19)

(0.0

18)

(0.0

18)

(0.0

20)

(0.0

20)

(0.0

20)

(0.0

20)

Fem

ale

0.9

43

**

0.9

60

**

0.9

64

**

0.9

84

**

1.0

09

**

1.0

24

**

1.0

25

**

1.0

39

**

(0.1

61)

(0.1

61)

(0.1

61)

(0.1

61)

(0.1

65)

(0.1

65)

(0.1

65)

(0.1

65)

Second

K.

1.9

82

**

2.0

09

**

1.9

26

**

2.0

98

**

1.8

39

**

1.8

69

**

1.8

29

**

2.0

15

**

(0.4

27)

(0.4

26)

(0.4

24)

(0.4

22)

(0.4

42)

(0.4

42)

(0.4

40)

(0.4

40)

Non-E

nglish

Lang.

at

Hom

e−

1.1

83

**

−1.3

25

**

−1.4

03

**

−1.6

65

**

−1.2

16

**

−1.3

40

**

−1.3

83

**

−1.5

63

**

(0.3

43)

(0.3

45)

(0.3

44)

(0.3

49)

(0.3

64)

(0.3

65)

(0.3

65)

(0.3

69)

Ste

pPare

nt

−0.9

25

**

−0.8

01

*−

0.7

73

*−

0.7

38

*−

0.7

05

*−

0.6

99

*(0

.317)

(0.3

20)

(0.3

20)

(0.3

25)

(0.3

29)

(0.3

29)

Sin

gle

Pare

nt

−0.6

37

**

0.0

80

0.0

47

−0.6

27

**

−0.0

25

−0.0

48

(0.2

21)

(0.2

31)

(0.2

37)

(0.2

29)

(0.2

38)

(0.2

43)

Oth

er

Fam

ily

Str

uctu

re−

0.7

65

−1.3

37

**

−1.3

26

**

−0.6

45

−0.8

73

−0.9

07

m(0

.428)

(0.4

64)

(0.4

62)

(0.4

38)

(0.4

76)

(0.4

74)

Child/A

dult

Rati

o-

1−

1.1

22

**

−1.0

71

**

−1.0

01

**

−0.9

64

**

(0.1

08)

(0.1

11)

(0.1

11)

(0.1

13)

Moth

er’

sA

ge

(cente

red)

0.0

56

**

0.0

53

**

0.0

28

0.0

27

(0.0

15)

(0.0

15)

(0.0

15)

(0.0

15)

Moth

er

Work

sPart

-Tim

e0.4

53

*0.3

27

(0.2

25)

(0.2

30)

Moth

er

Doesn

’tW

ork

0.8

62

**

0.7

79

**

(0.2

28)

(0.2

36)

Moth

er

Work

ed

Pri

or

toC

.B

irth

0.0

89

0.1

57

(0.2

03)

(0.2

09)

Hig

hSchoolor

Less

Ed.

Expecta

tions

−0.7

06

*−

0.6

04

m(0

.297)

(0.3

10)

Gra

duate

SchoolEd.

Expecta

tions

0.9

54

**

0.8

06

**

(0.1

87)

(0.1

94)

Hom

eB

ase

dC

are

0.7

21

**

0.6

38

*(0

.250)

(0.2

56)

Head

Sta

rt−

0.0

65

0.1

01

(0.3

28)

(0.3

47)

Cente

r-B

ase

dC

are

1.9

15

**

1.6

39

**

(0.2

25)

(0.2

39)

Note

:Table

conti

nued

on

nex

tpage.

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

246

Tab

leA

.12—

Con

tinu

ed:

Supp

lem

enta

ryM

odel

sfo

rR

eadi

ngA

chie

vem

ent

wit

hth

eFu

llC

ovar

iate

Lis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

Kin

derg

art

en

Slo

pe

1.8

49

**

1.8

72

**

1.8

80

**

1.8

89

**

1.8

68

**

1.8

68

**

1.8

69

**

1.8

68

**

(0.0

23)

(0.0

24)

(0.0

24)

(0.0

28)

(0.0

18)

(0.0

18)

(0.0

18)

(0.0

18)

Bla

ck

−0.1

90

**

−0.1

61

**

−0.1

53

**

−0.1

60

**

−0.1

79

**

−0.1

53

**

−0.1

46

**

−0.1

55

**

(0.0

39)

(0.0

40)

(0.0

40)

(0.0

40)

(0.0

46)

(0.0

47)

(0.0

47)

(0.0

47)

His

panic

−0.0

22

−0.0

16

−0.0

15

−0.0

23

−0.0

30

−0.0

25

−0.0

22

−0.0

28

(0.0

37)

(0.0

37)

(0.0

37)

(0.0

37)

(0.0

40)

(0.0

40)

(0.0

41)

(0.0

41)

Asi

an

0.1

64

**

0.1

65

**

0.1

69

**

0.1

66

**

0.1

44

*0.1

45

*0.1

48

*0.1

46

*(0

.055)

(0.0

55)

(0.0

55)

(0.0

55)

(0.0

59)

(0.0

59)

(0.0

59)

(0.0

59)

Oth

er

−0.0

15

−0.0

05

0.0

00

−0.0

01

−0.0

36

−0.0

27

−0.0

22

−0.0

23

(0.0

51)

(0.0

51)

(0.0

51)

(0.0

51)

(0.0

56)

(0.0

56)

(0.0

56)

(0.0

56)

Socia

lC

lass

0.1

05

**

0.0

99

**

0.0

95

**

0.0

87

**

0.1

13

**

0.1

06

**

0.1

03

**

0.0

95

**

(0.0

13)

(0.0

14)

(0.0

14)

(0.0

14)

(0.0

14)

(0.0

15)

(0.0

15)

(0.0

15)

Concert

ed

Cult

ivati

on

0.0

40

**

0.0

35

*0.0

33

*0.0

25

0.0

44

**

0.0

39

**

0.0

38

*0.0

30

*(0

.014)

(0.0

14)

(0.0

14)

(0.0

14)

(0.0

15)

(0.0

15)

(0.0

15)

(0.0

15)

Fem

ale

0.1

09

**

0.1

11

**

0.1

12

**

0.1

11

**

0.1

05

**

0.1

07

**

0.1

07

**

0.1

06

**

(0.0

21)

(0.0

21)

(0.0

21)

(0.0

21)

(0.0

21)

(0.0

21)

(0.0

21)

(0.0

21)

Second

K.

−0.2

91

**

−0.2

86

**

−0.2

86

**

−0.2

79

**

−0.2

88

**

−0.2

81

**

−0.2

82

**

−0.2

75

**

(0.0

55)

(0.0

55)

(0.0

55)

(0.0

56)

(0.0

58)

(0.0

57)

(0.0

58)

(0.0

58)

Non-E

nglish

Lang.

at

Hom

e−

0.0

02

−0.0

18

−0.0

19

−0.0

28

0.0

15

−0.0

01

−0.0

02

−0.0

08

(0.0

45)

(0.0

45)

(0.0

45)

(0.0

45)

(0.0

47)

(0.0

47)

(0.0

47)

(0.0

48)

Ste

pPare

nt

−0.0

43

−0.0

42

−0.0

41

−0.0

47

−0.0

42

−0.0

42

(0.0

40)

(0.0

41)

(0.0

41)

(0.0

41)

(0.0

42)

(0.0

41)

Sin

gle

Pare

nt

−0.0

94

**

−0.0

70

*−

0.0

71

*−

0.0

89

**

−0.0

67

*−

0.0

69

*(0

.028)

(0.0

30)

(0.0

30)

(0.0

29)

(0.0

31)

(0.0

31)

Oth

er

Fam

ily

Str

uctu

re−

0.1

26

*−

0.1

41

*−

0.1

32

*−

0.1

29

*−

0.1

52

*−

0.1

44

*(0

.055)

(0.0

59)

(0.0

59)

(0.0

56)

(0.0

60)

(0.0

60)

Child/A

dult

Rati

o-

1−

0.0

42

**

−0.0

39

**

−0.0

37

*−

0.0

34

*(0

.014)

(0.0

14)

(0.0

14)

(0.0

14)

Moth

er’

sA

ge

(cente

red)

0.0

01

0.0

01

0.0

02

0.0

02

(0.0

02)

(0.0

02)

(0.0

02)

(0.0

02)

Changed

School

−0.1

60

−0.1

51

−0.1

39

−0.1

32

−0.1

25

−0.1

18

−0.1

07

−0.0

99

(0.0

90)

(0.0

89)

(0.0

89)

(0.0

89)

(0.0

90)

(0.0

90)

(0.0

90)

(0.0

90)

Moth

er

Work

sPart

-Tim

e0.0

39

0.0

42

(0.0

27)

(0.0

28)

Moth

er

Doesn

’tW

ork

−0.0

15

−0.0

20

(0.0

25)

(0.0

25)

Hig

hSchoolor

Less

Ed.

Expecta

tions

−0.1

42

**

−0.1

44

**

(0.0

40)

(0.0

40)

Gra

duate

SchoolEd.

Expecta

tions

0. 0

07

0.0

03

(0.0

24)

(0.0

25)

Note

:Table

conti

nued

on

nex

tpage.

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

247

Tab

leA

.12—

Con

tinu

ed:

Supp

lem

enta

ryM

odel

sfo

rR

eadi

ngA

chie

vem

ent

wit

hth

eFu

llC

ovar

iate

Lis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

Sum

mer

Slo

pe

−0.3

11

**

−0.2

87

**

−0.2

66

**

−0.3

38

**

−0.1

79

**

−0.1

79

**

−0.1

79

**

−0.1

80

**

(0.0

82)

(0.0

87)

(0.0

87)

(0.0

99)

(0.0

56)

(0.0

56)

(0.0

56)

(0.0

56)

Bla

ck

0.1

92

0.1

98

0.2

10

0.2

12

0.2

86

0.2

72

0.2

82

0.2

85

(0.1

43)

(0.1

46)

(0.1

46)

(0.1

47)

(0.1

77)

(0.1

79)

(0.1

80)

(0.1

80)

His

panic

0.2

70

0.2

72

0.2

73

0.2

68

0.2

91

0.2

90

0.2

93

0.2

92

(0.1

47)

(0.1

47)

(0.1

47)

(0.1

48)

(0.1

65)

(0.1

65)

(0.1

65)

(0.1

66)

Asi

an

0.7

99

**

0.7

90

**

0.7

81

**

0.7

62

**

0.8

30

**

0.8

24

**

0.8

19

**

0.7

97

**

(0.2

25)

(0.2

25)

(0.2

25)

(0.2

26)

(0.2

40)

(0.2

41)

(0.2

41)

(0.2

41)

Oth

er

0.0

60

0.0

63

0.0

63

0.0

48

0.3

83

0.3

82

0.3

82

0.3

73

(0.1

80)

(0.1

80)

(0.1

80)

(0.1

81)

(0.2

13)

(0.2

13)

(0.2

14)

(0.2

14)

Socia

lC

lass

0.2

13

**

0.2

09

**

0.2

08

**

0.2

09

**

0.1

92

**

0.1

93

**

0.1

93

**

0.1

94

**

(0.0

53)

(0.0

54)

(0.0

55)

(0.0

55)

(0.0

58)

(0.0

59)

(0.0

60)

(0.0

60)

Concert

ed

Cult

ivati

on

0.0

61

0.0

54

0.0

51

0.0

43

0.0

52

0.0

50

0.0

49

0.0

41

(0.0

55)

(0.0

56)

(0.0

56)

(0.0

56)

(0.0

59)

(0.0

59)

(0.0

59)

(0.0

60)

Fem

ale

0.1

27

0.1

29

0.1

32

0.1

33

0.1

42

0.1

41

0.1

44

0.1

46

(0.0

83)

(0.0

83)

(0.0

83)

(0.0

83)

(0.0

84)

(0.0

84)

(0.0

85)

(0.0

85)

Second

K.

−0.1

77

−0.1

77

−0.1

73

−0.1

64

−0.1

36

−0.1

40

−0.1

37

−0.1

26

(0.2

26)

(0.2

26)

(0.2

25)

(0.2

26)

(0.2

35)

(0.2

35)

(0.2

34)

(0.2

34)

Non-E

nglish

Lang.

at

Hom

e−

0.0

86

−0.1

00

−0.1

03

−0.1

29

−0.1

41

−0.1

42

−0.1

43

−0.1

60

(0.1

76)

(0.1

77)

(0.1

77)

(0.1

78)

(0.1

88)

(0.1

88)

(0.1

89)

(0.1

89)

Ste

pPare

nt

−0.1

82

−0.1

93

−0.1

91

−0.1

30

−0.1

45

−0.1

46

(0.1

43)

(0.1

45)

(0.1

46)

(0.1

48)

(0.1

50)

(0.1

52)

Sin

gle

Pare

nt

−0.0

35

0.0

14

0.0

28

0.0

43

0.0

88

0.0

99

(0.1

03)

(0.1

08)

(0.1

09)

(0.1

07)

(0.1

12)

(0.1

13)

Oth

er

Fam

ily

Str

uctu

re−

0.0

54

−0.0

35

−0.0

21

0.0

35

0.0

70

0.0

84

(0.1

95)

(0.2

09)

(0.2

10)

(0.1

99)

(0.2

13)

(0.2

15)

Child/A

dult

Rati

o-

1−

0.0

93

−0.0

99

*−

0.0

87

−0.0

91

(0.0

50)

(0.0

50)

(0.0

52)

(0.0

52)

Moth

er’

sA

ge

(cente

red)

−0.0

02

−0.0

02

−0.0

02

−0.0

03

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

07)

Changed

School

−0.1

33

−0.1

14

−0.1

16

−0.1

03

−0.0

04

0.0

05

0.0

00

−0.0

01

(0.1

16)

(0.1

16)

(0.1

16)

(0.1

16)

(0.1

36)

(0.1

36)

(0.1

36)

(0.1

36)

Moth

er

Work

sPart

-Tim

e0.0

87

0.0

77

(0.0

87)

(0.0

91)

Moth

er

Doesn

’tW

ork

0.1

62

m0.1

32

(0.0

84)

(0.0

86)

Hig

hSchoolor

Less

Ed.

Expecta

tions

−0.0

51

−0.0

39

(0.1

23)

(0.1

25)

Gra

duate

SchoolEd.

Expecta

tions

0. 0

54

0.0

64

(0.0

78)

(0.0

81)

Note

:Table

conti

nued

on

nex

tpage.

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

248

Tab

leA

.12—

Con

tinu

ed:

Supp

lem

enta

ryM

odel

sfo

rR

eadi

ngA

chie

vem

ent

wit

hth

eFu

llC

ovar

iate

Lis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

1s

tG

rade

Slo

pe

3.4

39

**

3.4

60

**

3.4

63

**

3.5

20

**

3.3

67

**

3.3

67

**

3.3

67

**

3.3

68

**

(0.0

37)

(0.0

38)

(0.0

39)

(0.0

42)

(0.0

29)

(0.0

29)

(0.0

29)

(0.0

29)

Bla

ck

−0.3

04

**

−0.2

75

**

−0.2

72

**

−0.2

89

**

−0.1

77

*−

0.1

53

*−

0.1

52

*−

0.1

67

*(0

.063)

(0.0

64)

(0.0

64)

(0.0

64)

(0.0

74)

(0.0

75)

(0.0

75)

(0.0

75)

His

panic

−0.2

22

**

−0.2

15

**

−0.2

17

**

−0.2

31

**

−0.1

01

−0.0

94

−0.0

97

−0.1

08

(0.0

61)

(0.0

61)

(0.0

61)

(0.0

61)

(0.0

67)

(0.0

67)

(0.0

67)

(0.0

67)

Asi

an

−0.0

80

−0.0

74

−0.0

68

−0.0

82

−0.0

10

−0.0

04

0.0

00

−0.0

13

(0.0

91)

(0.0

91)

(0.0

91)

(0.0

91)

(0.0

96)

(0.0

96)

(0.0

96)

(0.0

96)

Oth

er

−0.1

11

−0.0

99

−0.0

94

−0.0

94

−0.0

08

0.0

04

0.0

06

0.0

04

(0.0

80)

(0.0

80)

(0.0

80)

(0.0

80)

(0.0

90)

(0.0

90)

(0.0

90)

(0.0

90)

Socia

lC

lass

0.1

45

**

0.1

39

**

0.1

38

**

0.1

15

**

0.1

20

**

0.1

15

**

0.1

16

**

0.0

92

**

(0.0

22)

(0.0

22)

(0.0

23)

(0.0

23)

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

25)

Concert

ed

Cult

ivati

on

0.1

16

**

0.1

12

**

0.1

11

**

0.0

92

**

0.0

93

**

0.0

90

**

0.0

90

**

0.0

72

**

(0.0

23)

(0.0

23)

(0.0

23)

(0.0

23)

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

24)

Fem

ale

0.0

73

*0.0

74

*0.0

74

*0.0

64

0.0

67

m0.0

69

*0.0

69

*0.0

59

(0.0

34)

(0.0

34)

(0.0

34)

(0.0

34)

(0.0

35)

(0.0

35)

(0.0

35)

(0.0

35)

Second

K.

−0.4

52

**

−0.4

42

*−

0.4

41

**

−0.4

14

**

−0.4

61

**

−0.4

52

**

−0.4

50

**

−0.4

23

**

(0.0

88)

(0.0

88)

(0.0

88)

(0.0

88)

(0.0

92)

(0.0

92)

(0.0

92)

(0.0

91)

Non-E

nglish

Lang.

at

Hom

e−

0.0

35

−0.0

53

−0.0

52

−0.0

81

0.0

47

0.0

31

0.0

32

0.0

03

(0.0

70)

(0.0

70)

(0.0

70)

(0.0

70)

(0.0

74)

(0.0

74)

(0.0

74)

(0.0

74)

Ste

pPare

nt

−0.0

41

−0.0

40

−0.0

34

−0.0

31

−0.0

34

−0.0

30

(0.0

55)

(0.0

56)

(0.0

56)

(0.0

56)

(0.0

57)

(0.0

58)

Sin

gle

Pare

nt

−0.0

76

−0.0

66

−0.0

65

−0.0

64

−0.0

57

−0.0

56

(0.0

40)

(0.0

43)

(0.0

43)

(0.0

42)

(0.0

44)

(0.0

44)

Oth

er

Fam

ily

Str

uctu

re−

0.1

98

*−

0.2

06

*−

0.1

81

*−

0.1

82

*−

0.1

78

*−

0.1

59

(0.0

84)

(0.0

88)

(0.0

88)

(0.0

85)

(0.0

89)

(0.0

90)

Child/A

dult

Rati

o-

1−

0.0

15

−0.0

09

−0.0

14

−0.0

08

(0.0

20)

(0.0

20)

(0.0

21)

(0.0

21)

Moth

er’

sA

ge

(cente

red)

0.0

00

0.0

00

−0.0

01

−0.0

01

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

03)

Changed

School

−0.3

76

**

−0.3

59

*−

0.3

55

**

−0.3

21

**

−0.3

37

**

−0.3

28

**

−0.3

25

**

−0.2

98

*(0

.113)

(0.1

13)

(0.1

13)

(0.1

12)

(0.1

16)

(0.1

16)

(0.1

16)

(0.1

15)

Moth

er

Work

sPart

-Tim

e0.0

01

−0.0

20

(0.0

33)

(0.0

33)

Moth

er

Doesn

’tW

ork

−0.0

43

−0.0

38

(0.0

32)

(0.0

34)

Hig

hSchoolor

Less

Ed.

Expecta

tions

−0.4

10

**

−0.4

06

**

(0.0

44)

(0.0

44)

Gra

duate

SchoolEd.

Expecta

tions

0.0

21

0.0

36

(0.0

34)

(0.0

34)

Note

:Table

conti

nued

on

nex

tpage.

‘m’p

<.0

6,‘*

’p

<.0

5,‘*

*’p

<.0

11

Model

’G’in

main

-chapte

ranaly

ses.

249

Tab

leA

.12—

Con

tinu

ed:

Supp

lem

enta

ryM

odel

sfo

rR

eadi

ngA

chie

vem

ent

wit

hth

eFu

llC

ovar

iate

Lis

tN

on

Gro

up-M

ean

Cente

red

Gro

up-M

ean

Cente

red

Coeffi

cie

nts

HI

JK

(G)1

HI

JK

(G)1

2n

d−

3r

dG

rade

Slo

pe

1.6

32

**

1.6

26

**

1.6

28

**

1.6

37

**

1.5

77

**

1.5

77

**

1.5

77

**

1.5

77

**

(0.0

12)

(0.0

13)

(0.0

13)

(0.0

15)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

09)

Bla

ck

−0.1

47

**

−0.1

46

**

−0.1

43

**

−0.1

53

**

−0.1

12

**

−0.1

14

**

−0.1

11

**

−0.1

18

**

(0.0

22)

(0.0

22)

(0.0

23)

(0.0

23)

(0.0

27)

(0.0

27)

(0.0

27)

(0.0

27)

His

panic

−0.0

35

−0.0

35

−0.0

35

−0.0

47

*−

0.0

09

−0.0

10

−0.0

08

−0.0

15

(0.0

21)

(0.0

21)

(0.0

21)

(0.0

21)

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

24)

Asi

an

−0.2

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BIBLIOGRAPHY

Abadie, Alberto, David Drukker, Jane Leber Herr, and Guido W. Imbens. 2004. “Im-plementing Matching Estimators for Average Treatment Effects in Stata.” StataJournal 4:290–311.

Aitkin, Murray. 1999. “A General Maximum Likelihood Analysis of Variance Compo-nents in Generalized Linear Models.” Biometrics 55:117–128.

Alexander, Karl L. 1997. “Public Schools and the Public Good.” Social Forces 76:1–30.

Alexander, Karl L., Doris R. Entwisle, and Susan L. Dauber. 1993. “First Grade Class-room Behavior: Its Short and Long-Term Consequences for School Performance.”Child Development 64:801–814.

Alexander, Karl L., Doris R. Entwisle, and Carrie S. Horsey. 1997. “From First GradeForward: Early Foundations of High School Dropout.” Sociology of Education70:87–107.

Alexander, Karl L., Doris R. Entwisle, and S. Linda Olson. 2001. “Schools, Achieve-ment, and Inequality: A Seasonal Perspective.” Educational Evaluation and PolicyAnalysis 23:171–191.

Allison, Paul D. 2001. Missing Data, volume 136 of Quantitative Applications in theSocial Sciences. Newbury Park: Sage Publications.

Alwin, Duane. 1984. “Trends in Parental Socialization Values.” American Journal ofSociology 90:359–382.

Anderson, Larui M. and Et Al. 2003. “The Effectiveness of Early Childhood DevelopmentPrograms.” American Journal of Preventive Medicine 24:32–46.

Applebee, Arthur N., Judith A. Langer, and Ina V.S. Mullis. 1988. Who Reads Best?Factors Related to Reading Achievement in Grades 3, 7, and 11 . Princeton, NJ:Educational Testing Service.

Aschaffenburg, Karen and Ineke Maas. 1997. “Cultural and Educational Careers: TheDynamics of Social Reproduction.” American Sociological Review 62:57387.

251

Barnett, W. Steven. 1998. “Long-Term Cognitive and Academic Effects of Early Child-hood Education on Children in Poverty.” Preventive Medicine 27:204–207.

Becker, Gary S. 1964. Human Capital . New York: Columbia University Press for theNational Bureau of Economic Research, 1st ed. edition.

Boardman, Jason D., Daniel A. Powers, Yolanda C. Padilla, and Robert A. Hummer.2002. “Lower Birth Weight, Social Factors, and Developental Outcomes amongChildren in the United States.” Demography 39:353–368.

Bollen, Kenneth A. 1989. Structural Equations with Latent Variables. Wiley Series inProbability and Mathematical Statistics Applied Probability and Statistics. NewYork: Wiley.

Bourdieu, Pierre. 1977a. “Cultural Reproduction and Social Reproduction.” In Powerand Ideology in Education, edited by Jerome Karabel and A. H. Haley, pp. 487–511.New York: Oxford University Press.

Bourdieu, Pierre. 1977b. Outline of the Theory of Practice. Cambridge, MA: CambridgeUniversity Press.

Bourdieu, Pierre. 1984. Distinction: A Social Critique of the Judgement of Taste. Cam-bridge, MA: Harvard University Press.

Bourdieu, Pierre. 1986. “The Forms of Capital.” In Handbook of Theory and Researchfor the Sociology of Culture, edited by John Richardson, pp. 241–258. New York:Greenwood Press.

Bourdieu, Pierre and Jean-Claude Passeron. 1977. Reproduction in Education, Society,and Culture. Beverly Hills, CA: Sage.

Bowles, Samuel and Herbert Gintis. 1976. Schooling in Capitalist America: EducationalReform and Contradictions of Economic Life. New York: Basic Books.

Bradley, Robert H. 1985. “The Home Inventory: Rational and Research.” In RecentResearch in Developmental Psychopathology, Book Supplement to the Journal ofChild Psychology and Psychiatry, edited by J. Lachenmeyer and M. Gibbs, pp.191–201. New York.

Bradley, Robert H. and Robert F. Corwyn. 2002. “Socioeconomic Status and ChildDevelopment.” Annual Review of Psychology 53:371–399.

Bradley, Robert H., Robert F. Corwyn, Harriette Pipes Mcadoo, and Cynthia Gar-cia Coll. 2001a. “The Home Environments of Children in the United States PartI: Variations by Age, Ethnicity, and Poverty Status.” Child Development 72:1844–1867.

Bradley, Robert H., Robert F. Corwyn, Harriette Pipes Mcadoo, and Cynthia Gar-cia Coll. 2001b. “The Home Environments of Children in the United States Part Ii:Relations with Behavioal Development through Age Thirteen.” Child Development72:1868–1886.

252

Bradley, Robert H., Stephen L. Rock, and Bettye M. Caldwell. 1987. “Home Environmentand School Performance among Black Elementary School Children.” The Journalof Negro Education 56:499–509.

Brooks-Gunn, Jeanne and Greg J. Duncan. 1997. “The Effects of Poverty on Children.”Future of Children 7:55–71.

Brooks-Gunn, Jeanne, Pamela K. Klebanov, and Greg J. Duncan. 1996. “Ethnic Differ-ences in Children’s Intelligence Test Scores: Role of Economic Deprivation, HomeEnvironment, and Maternal Characteristics.” Child Development 67:396–408.

Bryk, Anthony S. and Stephen W. Raudenbush. 1988. “Toward a More AppropriateConceptualization of Reserach on School Effects: A Three-Level Hierarchical LinearModel.” American Journal of Education 97:65–108.

Burkam, David T, Douglas D Ready, Valerie E Lee, and Laura F Logerfo. 2004. “Social-Class Differences in Summer Learning between Kindergarten and First Grade:Model Specification and Estimation.” Sociology of Education 77:1–31.

Cain, Glen G. and Arthur S. Goldberger. 1983. “Public and Private Schools Revisited.”Sociology of Education 56:208–218.

Carbonaro, William J. 1999. “Opening the Debate on Closure and Schooling Outcomes:Comment on Morgan and Sorensen.” American Sociological Review 64:682–686.

Carlson, Marcia J. and Mary E. Corcoran. 2001. “Family Structure and Children’sBehavioral and Cognitive Outcomes.” Journal of Marriage and the Family 63:779–792.

Chall, Jeanne Sternlicht. 1996a. Learning to Read : The Great Debate. Fort Worth:Harcourt Brace College Publishers, 3rd edition.

Chall, Jeanne Sternlicht. 1996b. Stages of Reading Development . Fort Worth: HarcourtBrace College Publishers, 2nd edition.

Chall, Jeanne Sternlicht, Vicki A. Jacobs, and Luke E. Baldwin. 1990. The ReadingCrisis : Why Poor Children Fall Behind . Cambridge, Mass.: Harvard UniversityPress.

Charity, Anne H., Hollis S. Scarborough, and Darion M. Griffin. 2004. “Familiarity withSchool English in African American Children and Its Relation to Early ReadingAchievement.” Child Development 75:1340–1356.

Charles, Camille Z., Gniesha Dinwiddie, and Douglass S. Massey. 2004. “The ContinuingConsequences of Segregation: Family Stress and College Academic Performance.”Social Science Quarterly 85:1353–1373.

Chin, Tiffani and Meredith Phillips. 2004. “Social Reproduction and Child-Rearing Prac-tices: Social Class, Children’s Agency, and the Summer Activity Gap.” Sociologyof Education 77:185–210.

253

Coleman, James S. 1972. “The Evaluation of Equality of Educational Opportunity.” InOn Equality of Educational Opportunity, edited by Frederick Mosteller and Daniel P.Moynihan, pp. 116–167. New York: Vintage Books, [1st ]. edition.

Coleman, James S. 1981. “Private Schools, Public Schools and the Public Interest.” ThePublic Interest 64:19–30.

Coleman, James S. 1988. “Social Capital in the Creation of Human Capital.” TheAmerican Journal of Sociology 94:95–120.

Coleman, James S., Thomas Hoffer, and Henry S. Levine. 1981. “Public and PrivateSchools.” Technical report, Center for Education Statistics.

Coleman, James S., Thomas Hoffer, and Henry S. Levine. 1982. “Cognitive Outcomesin Public and Private Schools.” Sociology of Education pp. 65–76.

Coleman, James S., United States. Office of Education., and National Center for Ed-ucation Statistics. 1966. Equality of Educational Opportunity. Washington]: U.S.Dept. of Health Education and Welfare Office of Education; [for sale by the Super-intendent of Documents U.S. Govt. Print. Off.

Condron, Dennis J. and Vincent J. Roscigno. 2003. “Disparities Within: SpendingInequality and Achievement in an Urban School District.” Sociology of Education76:18–36.

Congdon, Peter. 2003. Applied Bayesian Modelling . Hoboken: Wiley.

Cook, Michael D. and William N. Evans. 2000. “Families or Schools? Explaining theConvergence in White and Black Academic Performance.” Journal of Labor Eco-nomics 18:729–754.

Cooper, Harris, Barbara Nye, Kelly Charlton, James Lindsay, and Scott Greathouse.1996. “The Effects of Summer Vacation on Achievement Test Scores: A Narrativeand Meta-Analytic Review.” Review of Educational Research 66:227–268.

Davis, James Earl. 2003. “Early Schooling and Academic Achievement of African Amer-ican Males.” Urban Education 38:515–537.

De Boeck, Paul and Mark Wilson. 2004. Explanatory Item Response Models: A Gener-alized Linear and Nonlinear Approach. New York: Springer-Verlag.

De Graaf, Paul M. 1986. “The Impact of Financial and Cultural Resources on Educa-tional Attainment in the Netherlands.” Sociology of Education 59:237 246.

Degarmo, David S., Marion S. Forgatch, and Charles R. Martinez Jr. 1999. “Parenting ofDivorced Mothers as a Link between Social Status and Boys’ Academic Outcomes:Unpacking the Effects of Socioeconomic Status.” Child Development 70:1231–1245.

Dimaggio, Paul. 1982. “Cultural Capital and School Success: The Impact of StatusCulture Participation on the Grades of U.S. High School Students.” AmericanSociological Review 47:189201.

254

Dimaggio, Paul and John Mohr. 1985. “Cultural Capital, Educational Attainment, andMarital Selection.” American Journal of Sociology 90:12321261.

Downey, Douglas. 1995. “When Bigger Is Not Better: Family Size, Parental Resources,and Children’s Educational Performance.” American Sociological Review 60:746–761.

Downey, Douglas B., Paul T. Von Hippel, and Beckett Broh. 2004. “Are Schools theGreat Equalizer?” American Sociological Review 65:613–635.

Du Toit, M. (Ed.). 2003. Irt from Ssi: Bilog-Mg, Multilog, Parscale, Testfact . Lincol-nwood, IL: Scientific Software International.

Dumais, Susan A. 2002. “Cultural Capital, Gender, and School Success: The Role ofHabitus.” Sociology of Education 75:44–68.

Duncan, Greg J. and Jeanne Brooks-Gunn. 2000. “Family Poverty, Welfare Reform, andChild Development.” Child Development 71:188–196.

Duncan, Greg J., Jeanne Brooks Gunn, and Pamela Kato Klebanov. 1994. “EconomicDeprivation and Early Childhood Development.” Child Development 65:296–318.

Duvall, Evalyn M. 1946. “Conceptions of Parenthood.” American Journal of Sociology52:193–203.

Ensminger, Margaret E. and Anita L. Slusarcick. 1992. “Paths to High School Grad-uation or Dropout: A Longitudinal Study of a First-Grade Cohort.” Sociology ofEducation 65:95–113.

Entwisle, Doris R. and Karl L. Alexander. 1989. “Early Schooling as a ”Critical Period”Phenomenon.” In Sociology of Education and Socialization, edited by K. Nambood-iri and R. G. Corwin, volume 8. Greenwhich, Conn: JAI.

Entwisle, Doris R. and Karl L. Alexander. 1993. “Entry into School: The BeginningSchool Transition and Educational Stratification in the United States.” AnnualReview of Sociology 19:401–423.

Entwisle, Doris R., Karl L. Alexander, and Linda Steffel Olson. 1997. Children, Schools,and Inequality . Social Inequality Series. Boulder, Colo.: Westview Press.

Farkas, George. 1996. Human Capital or Cultural Capital? Ethnicity and Poverty Groupsin an Urban School District . Social Institutions and Social Change. New York: A.de Gruyter.

Farkas, George. 2003. “Cognitive Skills and Noncognitive Traits and Behaviors in Strat-ification Processes.” Annual Review of Sociology 29.

Farkas, George and Kurt Beron. 2001. “Family Linguistic Culture and Social Repro-duction: Verbal Skill from Parent to Child in the Preschool and School Years.”Paper Presented at the Annual Meetings of the Population Association of America,Washington, D.C., March 31, 2001 .

255

Farkas, George and Kurt Beron. 2004. “The Detailed Age Trajectory of Oral VocabularyKnowledge: Differences by Class and Race.” Social Science Research 33:464–497.

Farkas, George, Daniel Sheenan, Robert P. Grobe, and Yuan Shuan. 1990. “CulturalResources and School Success: Gender, Ethnicity, and Poverty Groups within anUrban School District.” American Sociological Review 55.

Fryer Jr., Roland G. and Steven D. Levitt. 2004. “Understanding the Black-WhiteTest Score Gap in the First Two Years of School.” The Review of Economics andStatistics 86:447–464.

Ganzeboom, Harry B.G., Paul M. De Graaf, and Peter Robert. 1990. “ReproductionTheory on Socialist Ground: Intergenerational Transmission of Inequalities in Hun-gary.” In Research in Social Stratification and Mobility, edited by Arne L. Kalleberg,pp. 3–84. Greenwhich, CT: JAI Press.

Gelman, Andrew, John B. Carlin, Hal S. Stern, and Donald B. Rubin. 2003. BayesianData Analysis. London: Chapman & Hall, 2nd edition.

Gilliam, Walter S. and Edward F. Zigler. 2000. “A Critical Meta-Analysis of All Evalua-tions of State-Funded Preschool from 1977 to 1998: Implications for Policy, ServiceDelivery and Program Evaluation.” Early Childhood Research Quarterly 15:441–473.

Goldberger, Arthur S. and Glen G. Cain. 1982. “The Causal Analysis of CognitiveOutcomes in the Coleman, Hoffer and Kilgore Report.” Sociology of Education55:103–122.

Granovetter, Mark. 1985. “Economic Action and Social Structure: The Problem ofEmbeddedness.” The American Journal of Sociology 91:481–510.

Grissmer, David, Ann Flanagan, and Stephanie Williamson. 1998. “Why Did the Black-White Score Gap Narrow in the 1970s and 1980s?” In The Black-White Test ScoreGap, edited by Christopher Jencks and Meredith Phillips, pp. 182–228. Washington,D.C.: Brookings Institution Press.

Guo, Guang. 1998. “The Time of the Influences of Cumulative Poverty on ChildrensCognitive Ability and Achievement.” Socal Forces 71:257–288.

Guo, Guang and Kathleen Mullan Harris. 2000. “The Mechanisms Mediating the Effectsof Poverty on Children’s Intellectual Development.” Demography 37:431–447.

Guo, Guang and Elizabeth Stearns. 2002. “The Social Influences on the Realization ofGenetic Potential for Intellectual Development.” Social Forces 80:881–910.

Hallinan, Maureen T. and Warren N. Kubitschek. 1999. “Conceptualizing and MeasuringSchool Social Networks: Comment on Morgan and Sorensen.” American SociologicalReview 64:687–693.

256

Hart, Betty and Todd R. Risley. 1995. Meaningful Differences in Everyday Experienceof Young American Children. Baltimore: Paul Brookes Publishing Co.

Heath, Shirley Brice. 1983. Ways with Words. Cambridge: Cambridge University Press.

Hedges, Larry V. and Amy Nowell. 1998. “Black-White Test Score Convergence since1965.” In The Black-White Test Score Gap, edited by Christopher Jencks andMeredith Phillips, pp. 149–181. Washington, D.C.: Brookings Institution Press.

Herrnstein, Richard J. and Charles A. Murray. 1994. The Bell Curve : Intelligence andClass Structure in American Life. New York: Free Press.

Hodson, Randy and Borbert L. Kaufman. 1982. “Economic Dualism: A Critical Review.”American Sociological Review 47:727–739.

Honig, Bill. 1996. Teaching Our Children to Read: The Role of Skills in a ComprehensiveReading Program. Thousand Oaks, CA: Corwin Press, Inc.

Horvat, Erin M., Elliott B. Weininger, and Annette Lareau. 2003. “From Social Tiesto Social Capital: Class Differences in the Relations between Schools and ParentNetworks.” American Educational Research Journal 40:319351.

Imbens, Guido W. 2004. “Nonparametric Estimation of Average Treatment Effects underExogeneity: A Review.” The Review of Economics and Statistics 86:4–29.

Jencks, Christoper. 1998. “Racial Bias in Testing.” In The Black-White Test Score Gap,edited by Christopher Jencks and Meredith Phillips, pp. 55–85. Washington, D.C.:Brookings Institution Press.

Jencks, Christoper and Meredith Phillips. 1998. “The Black-White Test Score Gap: AnIntroduction.” In The Black-White Test Score Gap, edited by Christopher Jencksand Meredith Phillips, pp. 1–44. Washington, D.C.: Brookings Institution Press.

Jimerson, Shane, Byron Egeland, L. Alan Sroufe, and Betty Carlson. 2000. “A Prospec-tive Longitudinal Study of High School Dropouts Examining Multiple Predictorsacross Development.” Journal of School Psychology 38:525–549.

Kao, Grace and Jennifer S. Thompson. 2003. “Racial and Ethnic Stratification in Edu-cational Achievement and Attainment.” Annual Review of Sociology 49:417–442.

Kerckhoff, Alan C., Stephen W. Raudenbush, and Elizabeth Glennie. 2001. “Education,Cognitive Skill, and Labor Force Outcomes.” Sociology of Education 74:1–24.

Kingston, Paul W. 2001. “The Unfulfilled Promise of Cultural Capital Theory.” Sociologyof Education Extra Issue 2001, Currents of Thought: Sociology of Education at theDawn of the 21st Century::88–99.

Kohn, Melvin L. 1959. “Social Class and Parental Values.” American Journal of Soci-ology 64:337–351.

257

Kohn, Melvin L. 1977. Class and Conformity : A Study in Values. Chicago: Universityof Chicago Press, 2d edition.

Kohn, Melvin L. and Carmi Schooler. 1969. “Class, Occupation and Orientation.” Amer-ican Sociological Review 34:659–678.

Kohn, Melvin L. and Carmi Schooler (Eds). 1983. Work and Personality: An Inquiryinto the Impact of Social Stratification. Norwood, NJ: Ablex.

Kozol, Jonathan. 1991. Savage Inequalities. New York: Crown Publishers.

Lamont, Michle. 1992. Money, Morals, and Manners: The Culture of the French andthe American Upper-Middle Class. Chicago: University of Chicago Press.

Lamont, Michle and Annette Lareau. 1988. “Cultural Capital: Allusions, Gaps andGlissandos in Recent Theoretical Developments.” Sociological Theory 6:153–168.

Landry, Susan H., Karen E. Smith, Cynthia L. Miller-Loncar, and Paul R. Swank. 1997.“Predicting Cognitive-Language and Social Growth Curves from Early MaternalBehaviors in Children at Varying Degrees of Biological Risk.” Developmental Psy-chology 33:1040–1053.

Lapp, M.S., W.S. Grigg, and B.S.-H. Tay-Lim. 2002. “The NationS Report Card: U.S.History 2001.” Technical Report 2001, NCES 2002483, U.S. Department of Educa-tion. Office of Educational Research and Improvement. National Center for Educa-tion Statistics.

Lareau, Annette. 1989. Home Advantage : Social Class and Parental Intervention inElementary Education. Education Policy Perspectives Social Analysis Series. NewYork: Falmer Press.

Lareau, Annette. 2002. “Invisible Inequality: Social Class and Childrearing in BlackFamilies and White Families.” American Sociological Review 67:747–776.

Lareau, Annette. 2003. Unequal Childhoods: Class, Race, and Family Life. Berkely:University of California Press.

Lin, Nan. 2001. Social Capital: A Theory of Social Structure and Action. Cambridge:University Press.

Little, Roderick .J.A. and Donald B. Rubin. 1987. Statistical Analysis with MissingData. New York: John Wiley.

Lynd, Robert S. and Helen Merrell Lynd. 1929. Middletown: A Study in ContemporaryAmerican Culture. New York: Harcourt, Brace.

Macleod, Jay. 1995. Ain’t No Maken’ It: Aspirations and Attainment in a Low-IncomeNeighborhood . Boulder, CO: Westview Press.

Marsh, Lawrence C. and David R. Cormier. 2002. Spline Regression Models. ThousandOaks, CA: Sage Publications, Inc.

258

Massey, Douglas S. 2004. “Segregation and Stratification: A Biosocial Perspective.”DuBois Review: Social Science Research on Race 1:1–19.

Massey, Douglas S. and Nancy A. Denton. 1993. American Apartheid: Segregation andthe Making of the Underclass. Cambridge: Harvard University Press.

Massey, Douglas S. and Mary J. Fischer. 2002. “The Long Term Consequences of Segre-gation.” In The Annual Meetings of the Population Association of America, Atlanta,GA.

Mayer, Susan. 1997. What Money Can’t Buy: Family Income and Children’s LifeChances. Cambridge: Harvard University Press.

Mcclelland, Megan M. and Frederick J. Morrison. 2003. “The Emergence of Learning-Related Social Skills in Preschool Children.” Early Childhood Research Quarterly18:206–224.

Mcloyd, Vonnie C. 1998. “Socioeconomic Disadvantage and Child Development.” Amer-ican Psychologist 53:185–204.

Mills, C. Wright. 1956. The Power Elite. New York,: Oxford University Press.

Morgan, Stephen L. and Aage B. Sorensen. 1999a. “Parental Networks, Social Closure,and Mathematics Learning: A Test of Coleman’s Social Capital Explanation ofSchool Effects.” American Sociological Review 64:661–681.

Morgan, Stephen L. and Aage B. Sorensen. 1999b. “Theory, Measurement, and Speci-fication Issues in Models of Network Effects on Learning: Reply to Carbonaro andto Hallinan and Kubitschek.” American Sociological Review 64:694–700.

Mueller, Charles W. and Toby L. Parcel. 1981. “Measures of Socioeconomic Status:Alternatives and Recommendations.” Child Development 52:13–30.

MunthEN, Bengt O. and Linda K. MunthEN. 2004. Mplus Users Guide. Third Edition.Los Angeles: Munthen and Munthen.

Murnane, Richard J., John B. Willett, M. Jay Braatz, and Yves Duhaldeborde. 2001. “DoDifferent Dimensions of Male High School Students’ Skills Predict Labor MarketSuccess a Decade Later? Evidence from the Nlsy.” Economics of Education Review20:311–320.

Nces, the National Center for Education Statistics. 2000. “Ecls-K Base Year Data Filesand Electronic Codebook.” Technical report, U.S. Department of Education.

Nces, the National Center for Education Statistics. 2002. “Early Childhood Longitu-dinal Study - Kindergarten Class of 1998-99 (Ecls-K), Psychometric Report forKindergarten through First Grade.” Technical Report 200205, U.S. Department ofEducation.

259

Nces, the National Center for Education Statistics. 2004. “User’s Manual for the Ecls-KThird Grade Public-Use Data Files and Electronic Code Book.” Technical Report2004001, U.S. Department of Education.

Ogbu, John. 1974. The Next Generation: An Ethnography of Education in an UrbanNeighborhood . New York: Academic Press.

Ogbu, John. 1978. Minority Education and Caste: The American System in Cross-Cultural Perspective. Orlando, FL: Academic Press.

Ogbu, John. 1986. “The Consequences of the American Caste System.” In The SchoolAchievement of Minority Children, edited by U. Neisser. Hillsdale, NJ: LawrenceErlbaum.

Parcel, Toby L. and Elizabeth G. Menaghan. 1990. “Maternal Working Conditions andChild Verbal Ability: Studying the Transmission of of Intergenerational Inequalityfrom Mothers to Young Children.” Social Psychology Quarterly 53:132–147.

Parcel, Toby L. and Elizabeth G. Menaghan. 1994a. “Early Parental Work, FamilySocial Capital, and Early Childhood Outcomes.” American Journal of Sociology99:972–1009.

Parcel, Toby L. and Elizabeth G. Menaghan. 1994b. Parents’ Jobs and Children’s Lives.Sociology and Economics. New York: A. de Gruyter.

Phillips, Meredith, Jeanne Brooks-Gunn, Greg J. Duncan, Pamela Klebanov, andJonathan Crane. 1998a. “Family Background, Parenting Practices, and the Black-White Test Score Gap.” In The Black-White Test Score Gap, edited by ChristopherJencks and Meredith Phillips, pp. 103–148. Washington, D.C.: Brookings Institu-tion Press.

Phillips, Meredith, Mames Crouse, and James Ralph. 1998b. “Does the Black-WhiteTestscore Gap Widen after Children Enter School?” In The Black-White Test ScoreGap, edited by Christopher Jencks and Meredith Phillips, pp. 229–272. Washington,D.C.: Brookings Institution Press.

Portes, Alejandro. 1988. “Social Capital: Its Origins and Applications in Modern Soci-ology.” American Review of Sociology 24:1–24.

Portes, Alejandro. 1998. “Social Capital: Its Origins and Applications in Modern Soci-ology.” Annual Review of Sociology 24:1–24.

Portes, Alejandro. 2000. “The Two Meanings of Social Capital.” Sociological Forum15:1–12.

Raudenbush, Stephen and Robert J. Sampson. 1999. “’Ecometrics’: Toward a Scienceof Assessing Ecological Settings, with Application to the Systematic Social Obser-vation of Neighborhoods.” Sociological Methodology 29:1–41.

260

Raudenbush, Stephen W. and Anthony S. Bryk. 2002. Hierarchical Linear Models: Ap-plications and Data Analysis Methods. Thousand Oaks, CA: Sage Publications,Inc.

Raudenbush, Stephen W. and Rafa M. Kasim. 1998. “Cognitive Skill and Economic In-equality: Findings from the National Adult Literacy Survey.” Harvard EducationalReview 68:33–79.

Reardon, Sean F. 2003. “Sources of Educational Inequality: The Growth ofRacial/Ethnic and Socioeconomic Test Score Gaps in Kindergarten and FirstGrade.” Under Review .

Reardon, Sean F., John T. Yun, and Tamela Mcnulty Eitle. 2000. “The Changing Struc-ture of School Segregation: Measurement and Evidence of Multiracial Metropolitan-Area School Segregation, 1989-1995.” Demography 37:351–364.

Roscigno, Vincent J. 1998. “Race and the Reproduction of Educational Disadvantage.”Social Forces 76:1033–61.

Rosenbaum, P. R. and D. B. Rubin. 1983. “The Central Role of the Propensity Score inObservational Studies for Causal Effects.” Biometrika 70:41–55.

Royston, Patrick. 2004. “Multiple Imputation of Missing Values.” Stata Journal 4:227–241.

Sampson, Robert J. and W. Byron Groves. 1989. “Community Structure and Crime:Testing Social-Disorganization Theory.” American Journal of Sociology 94:774–802.

Schultz, Theodore W. 1961. “Education and Economic Growth.” In Social Forces In-fluencing American Education, edited by N. B. Henry. Chicago, IL: University ofChicago Press.

Schultz, Theodore W. 1963. The Economic Value of Education. New York: ColumbiaUniversity Press.

Senechal, Monique and Jo Anne Lefevre. 2002. “Parental Involvement in the Develop-ment of Children’s Reading Skill: A Five-Year Longitudinal Study.” Child Devel-opment 73:445–460.

Skrondal, Anders and Sophia Rabe-Hesketh. 2004. Generalized Latent Variable Modeling:Multilevel, Longitudinal, and Structural Equation Models. New York: Chapman &Hall/CRS.

Smith, Judith R., Jeanne Brooks-Gunn, and Pamela Klebanov. 1997. “Consequencesof Living in Poverty for Young Children’s Cognitive and Verbal Ability and EarlySchool Achievement.” In Consequences of Growing up Poor , edited by Greg J. Dun-can and Jeanne Brooks-Gunn, pp. 132–189. New York: Russell Sage Foundation.

261

Smith, Karen E., Susan H. Landry, and Paul R. Swank. 2000. “Does the Contentof Mothers’ Verbal Stimulation Explain Differences in Children’s Development ofVerbal and Nonverbal Cognitive Skills?” Journal of School Psychology 38:27–49.

Snow, Catherine E., Susan M. Burns, and Peg Griffin. 1998. Preventing Reading Dif-ficulties in Young Children. Committee on the Prevention of Reading Difficulties.Washington, D.C.: National Academy Press.

Stipek, D. J. and R.R. Ryan. 1997. “Economically Disadvantaged Preschoolers: Readyto Learn but Further to Go.” Developmental Psychology 33:711723.

Sue, Stanley and Sumie Okazaki. 1990. “Asian-American Educational Achievements: APhenomenon in Search of an Explanation.” The American Psychologist 45:913–921.

Sun, Yongmin. 1998. “The Academic Success of East-Asian-American Students–an In-vestment Model.” Social Science Research 27:432–456.

Swidler, Anne. 1986. “Culture in Action: Symbols and Strategies.” American SociologicalReview 51:237–286.

Teachman, Jay. 1997. “Gender of Siblings, Cognitive Achievement, and Academic Per-formance: Familial and Nonfamilial Influences on Children.” Journal of Marriageand the Family 59:363–374.

Teachman, Jay D. 1996. “Intellectual Skill and Academic Performance: Do Families Biasthe Relationship?” Sociology of Education 69:35–48.

Van Der Linden, Wim J. and Ronald K. Hambleton. 1997. “Item Response Theory: BriefHistory, Common Models, and Extensions.” In Handbook of Modern Item ResponseTheory , edited by Wim J. v. d. Linden and Ronald K. Hambleton, pp. 1–28. NewYork: Spring-Verlag.

Votruba-Drzal, Elizabeth. 2003. “Income Changes and Cognitive Stimulation in YoungChildren’s Home Learning Environments.” Journal of Marriage and the Family64:341–355.

Wilson, William Julius. 1996. When Work Disappears: The World of the New UrbanPoor . New York: Vintage Books.

Yinger, John. 1995. Closed Doors, Opportunities Lost: The Continuing Costs of HousingDiscrimination. New York: Russell Sage Foundation.

Zhou, Min and Carl L. Bankston Iii. 1998. Growing up American. New York: RussellSage Foundation.

Vita

Jacob E. Cheadle

Academic Positions

· Robert Wood Johnson Scholars in Health Policy Postdoctoral Researcher, Cohort II for2005-2007

Academic Qualifications· M.A., Sociology and Demography, Western Washington University, 2000.Thesis: Racial Differences in the Formation of Mobility Expectations.Committee: Kyle D. Crowder, Jay D. Teachman, & Lucky M. Tedrow. Summer Programin Quantitative Methods of Social Research, June – August 2002.· B.S., Sociology and Demography, Western Washington University, 1998Thesis: Ethnic Birthweight Differentials for Washington State, 1995-1997.Advisor: Lucky M. Tedrow.

Published Papers· Amato, Paul R., and Jacob E. Cheadle. 2005. “The Long Reach of Divorce: Implicationsof Marital Dissolution for Three Generations.” Journal of Marriage and Family, 67(1):191-206.· Handcock, Mark S., Michael S. Rendall, and Jacob E. Cheadle. Forthcoming. “ImprovedRegression Estimation of a Multivariate Relationship with Population Data on the Bivari-ate Relationship.” Sociological Methodology.

Submitted Papers· Amato, Paul R., and Jacob E. Cheadle. Submitted. “The Impact of Marital Discord onBiological and Adopted Children’s Behavioral Outcomes.”· Stout, Michael S., and Jacob E. Cheadle. 2004. “Class-Size and Student AcademicAchievement Over the Early Grades.”

Conference Presentations· Cheadle, Jacob E., and Paul R. Amato. 2002. “The Transmission of Divorce Across ThreeGenerations: The Role of Interpersonal Behavior and Commitment to Marriage”, Posterpresented at the 2002 annual PAA meetings in Atlanta, May 9-11,· Rendall, Michael R., Mark S. Handcock, Mark S., and Jacob E. Cheadle. 2002. “Combin-ing Panel and Registration Data to Estimate Divorce Probabilities as part of Mens FamilyLife Course.” Course. Invited Paper, Workshop on the Analysis of Incomplete Data inMultistate Models, Johns Hopkins University.

Awards & Honors· Research and Graduate Studies Office Dissertation Support ($1300)· AERA Dissertation Grant ($15,000), 2004-2005· AERA Institute on Statistical Analysis for Education Policy, April 2004· Clogg Scholarship, ICPSR, Summer 2002· NICHD Demography Trainee through The Population Research Institute at the Pennsyl-vania State University in August, 2001 to August, 2003· Demographic Research Scholarship from the Demographic Research Lab at Western Wash-ington University for outstanding scholarship, 1999