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Volume 83 Number 1 January 2010

ContentsAcademic Vulnerability and Resilience during the Transition to High School: The Role of Social Relationships and District Context Amy G. Langenkamp 1

After the Bell: Participation in Extracurricular Activities, Classroom Behavior, and Academic Achievement Elizabeth Covay and William Carbonaro 20

Parenting for Cognitive Development from 1950 to 2000: The Institutionalization of Mass Education and the Social Construction of Parenting in the United StatesMaryellen Schaub 46

Cultural Capital in East Asian Educational Systems: The Case of Japan Yoko Yamamoto and Mary C. Brinton 67

Sociology of Education

Editor

David B. Bills University of Iowa

Deputy Editors

Members

Richard Arum New York University

Hanna Ayalon Tel Aviv University

Carl L. Bankston, III Tulane University

Mark A. Berends University of Notre Dame

David B. Bills University of Iowa

Prudence L. Carter Stanford University

Elizabeth C. Cooksey Ohio State University

Robert Crosnoe University of Texas at Austin

Scott Davies McMaster University

Regina Deil-Amen University of Arizona

John B. Diamond Harvard Univeristy

Thomas A. DiPrete Columbia University

Susan A. Dumais Louisiana State University

Danielle Cireno Fernandes Universidade Federal

De Minas Gerais

Eric Grodsky University of Minnesota

Angel Luis Harris Princeton University

Sean Kelly University of Notre Dame

Spyros Konstantopoulos Northwestern University

Kevin T. Leicht The University of Iowa

Freda B. Lynn University of Iowa

Vida Maralani Yale University

Hugh Mehan University of California-San Diego

Lynn M. Mulkey University of South Carolina,

Beaufort

Chandra Muller University of Texas

Stephen B. Plank Johns Hopkins University

Alejandro PortesPrinceton University

Sean F. Reardon Stanford University

Josipa Roksa University of Virginia

Evan SchoferUniversity of California, Irvine

John R. Schwille Michigan State University

Tricia Seifert University of Toronto

Mitchell L. Stevens Stanford University

Tony Tam Chinese University of Hong Kong

and Academia Sinica

Edward E. Telles Princeton University

Marta Tienda Princeton University

Ruth N. Lopez Turley University of Wisconsin

Sarah Turner University of Virginia

Karolyn Tyson University North Carolina-Chapel Hill

Herman G. Van De Werfhorst University of Amsterdam

Sociology of Education

Stefanie Ann DeLuca Johns Hopkins University

Stephen L. MorganCornell University

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...issues that both faculty and students are expected to address (DeMartini 1983; Lynch and Smith 1985; Rippertoe 1977).

After the Bell: Participation inExtracurricular Activities,Classroom Behavior, andAcademic Achievement

Elizabeth Covay1 and William Carbonaro1

Abstract

Prior research has not examined how much of the socioeconomic status (SES) advantage on schoolingoutcomes is related to participation in extracurricular activities. The authors explore the SES advantageand extracurricular participation in elementary school–aged children, with a focus on noncognitive skills.The authors argue that noncognitive skills mediate the influence of SES and extracurricular activities onacademic skills. Using data from the Early Childhood Longitudinal Study–Kindergarten Class of 1998–99,the authors find that extracurricular participation explains a modest portion of the SES advantage in non-cognitive and cognitive skills. In addition, the influence of extracurricular participation on both noncogni-tive and cognitive skills varies by children’s SES.

Keywords

extracurricular activities, noncognitive skills, achievement, SES advantage

Socioeconomic status (SES) differences in educa-

tional achievement and attainment are large and

pervasive in modern industrialized societies.

Students from higher-SES backgrounds have higher

levels of academic achievement and are more likely

to go further in school than lower SES students.

These SES inequalities in schooling outcomes are

later translated into advantages in occupational attain-

ment and income (Blau and Duncan 1967; Jencks

1972; Kerckhoff, Raudenbush, and Glennie 2001;

Sewell and Hauser 1975). A growing body of evi-

dence suggests that SES gaps in achievement are pres-

ent before students enter formal schooling (Entwisle,

Alexander, and Olson 1997; Farkas 2004; Hart and

Risley 1995). These important findings suggest that

inequalities in students’ home environments are criti-

cal factors that drive much of the SES gap in achieve-

ment in school. Our study contributes to the SES gap

literature by examining inequalities in an additional

context: extracurricular activities (EAs).

In this study, we focus on unequal access to

learning opportunities that elementary school stu-

dents receive outside both the conventional school

curriculum and the immediate home environment.

We examine whether EAs provide an additional

source of advantage for high-SES students that

helps them increase their chances of school suc-

cess. In her recent ethnographic account of class

differences in childhood experiences, Lareau

(2003) focused on class differences in parenting

styles that led some parents to provide enriched

extracurricular experiences for their children. In

our study, we examine SES differences in extra-

curricular participation in elementary school and

consider their effect on students’ noncognitive

skills and achievement outcomes for students in

the same age range as Lareau’s study.

We argue that EAs improve students’ noncog-

nitive skills: a broad set of skills that include (but

1University of Notre Dame, Notre Dame, IN, USA

Corresponding Author:

Elizabeth Covay, University of Notre Dame, Department of

Sociology, 810 Flanner Hall, Notre Dame, IN 46556, USA

Email: [email protected]

Sociology of Education83(1) 20–45

� American Sociological Association 2010DOI: 10.1177/0038040709356565

http://soe.sagepub.com

are not limited to) task persistence, independence,

following instructions, working well within

groups, dealing with authority figures, and fitting

in with peers (i.e., skills that align with the ‘‘hid-

den curriculum’’; Carneiro and Heckman 2005;

Dreeben 1968; Farkas 2003; Jackson 1968;

Rosenbaum 2001). We focus on noncognitive

skills as the mechanism that explains the link

between extracurricular participation and

increased academic achievement. Our results indi-

cate that students from higher-SES families do

participate in EAs more than students from

lower-SES families. We also find that race and

the percentage of minority students within a school

are related to a student’s likelihood of extracurric-

ular participation. Overall, participation in EAs

explains a modest portion of the SES advantage

in both noncognitive and cognitive skills.

Finally, the association between extracurricular

participation on noncognitive and cognitive skills

depends in part on students’ SES.

Unequal Participation in EAs

When examining EAs, it is helpful to differentiate

between structured and unstructured activities.

Structured EAs are organized with a focus on skill

building (Gilman, Meyers, and Perez 2004) and

social and/or behavioral goals (Fletcher,

Nickerson, and Wright 2003). Unstructured EAs

are spontaneous and informal (Fletcher et al.

2003). Compared with other literate postindustrial

countries, children in the United States spend

a large amount of time in leisure activities, with

more than half of children’s waking hours spent

in leisure activities (Larson and Verma 1999).1

Eighty percent of children participate in organized

activities, yet the majority of all students’ leisure

time is spent in unstructured activities

(Mahoney, Harris, and Eccles 2006). In this study,

we focus on structured EAs because (as we

describe below) these activities are most likely

to contribute to the development of noncognitive

skills and greater student learning.

Prior research has suggested that participation

in EAs varies by family background (Dumais

2006; Lareau 2003). In her in-depth ethnographic

study, Lareau (2003) found important class differ-

ences in how students spent their leisure time:

upper- and middle-class students had little

unscheduled time and spent more time in struc-

tured EAs, whereas lower- and working-class

students mostly participated in unstructured

activities. Dumais (2006) analyzed nationally rep-

resentative data and found that SES was positively

related to extracurricular participation.2

Interestingly, little research has examined why

social class is related to extracurricular participa-

tion. Lareau (2003) identified two distinct parent-

ing styles in her study, concerted cultivation and

accomplishment of natural growth, which were

related to EA participation.3 Lareau found that

high-SES families pursued concerted cultivation,

whereas lower-SES families embraced a natural

growth approach. Thus, Lareau offered a cultural

explanation for SES differences in extracurricular

participation. Chin and Phillips (2004) challenged

Lareau’s conclusion with their study of how pa-

rents organized summer activities for their chil-

dren. In their study, Chin and Phillips found that

low-SES parents valued these EAs during the

summer (just as high-SES parents did), but

income and time constraints served as significant

barriers that lowered participation rates for low-

SES families. Because neither study used a nation-

ally representative sample or statistical controls to

estimate associations between SES and extracur-

ricular participation, additional research is needed

to disentangle the effects of parental education,

occupation, and income on extracurricular

participation.

Prior research has also indicated that race is

a significant predictor of participation in EAs.

Dumais (2006) found that black and Hispanic

children participated in EAs in kindergarten or

first grade at a lower rate compared to white chil-

dren. In testing the larger construct of concerted

cultivation, Cheadle (2008) found significant

racial differences in concerted cultivation,

although he did not focus specifically on extracur-

ricular participation. Yet Lareau (2003) concluded

that, after accounting for class differences, there

were no racial differences in her study.

As with SES, little is known about why race is

related to extracurricular participation. Although

Lareau (2003) argued that racial differences are

explained by differences in SES, we suspect that

continued residential and school segregation

(Clotfelter 2006) creates different levels of access

to extracurricular opportunities by SES and race.

Research (see Eccles et al. 2003) suggests that

at-risk adolescents tend to have less access to

high-quality extracurricular programs. Moreover,

Pattillo-McCoy (2005) found that middle-class

black families live in more disadvantaged neigh-

borhoods compared with middle-class and poor

Covay and Carbonaro 21

white families, which suggests that black families

have less access to community resources.4 Once

again, research with nationally representative

data and multivariate models can help us better

understand why race is related to extracurricular

participation.

EAs and Noncognitive Skills

Unequal participation in EAs takes on greater sig-

nificance when we consider possible linkages

between extracurricular participation and academic

outcomes. If EAs improve student achievement, in-

equalities in participation may contribute to SES

and racial-ethnic gaps in learning gains. Figure 1

presents the conceptual model that motivates our

analyses. The model begins with the (already dis-

cussed) link between student background and

extracurricular participation (line a). We hypothe-

size that EAs contribute to student achievement

indirectly by enhancing students’ noncognitive

skills (line b), which produces greater gains in stu-

dents’ learning (line c).5 In this section, we argue

for the importance of line b in the figure: the link

between EAs and students’ noncognitive skills.

EAs resemble classroom settings in many

important ways. Both settings promote and incul-

cate similar values among children. Dreeben

(1968) identified four main values promoted in

classrooms: independence, achievement, univer-

salism, and specificity. Some EAs, such as sports

and music, strongly promote and value achieve-

ment: children must demonstrate mastery of

a given set of skills by performing in public (or

semipublic) settings in which they are evaluated

by others (Lareau 2003). Children must regularly

deal with success and failure (e.g., winning and

losing, missing notes and cues) in EAs, just as

they do in the classroom (Lareau 2003). Task per-

sistence and a strong work ethic are also important

in both classroom and extracurricular settings.

Being on a team, in an orchestra, or in the cast

of a play typically involves being a member of

a general category (e.g., soccer player, percussion-

ist), and participants are typically given specific

roles to fulfill. These experiences promote the val-

ues of universalism and specificity (respectively).

EAs also resemble classroom environments in

how social relationships are defined and struc-

tured. In both cases, children are subordinate to

an adult authority figure who sets goals and ex-

pectations for children, organizes tasks designed

to promote mastery of a given skill, and provides

instruction to promote skill development. Success

in both the classroom and EAs requires an ability

to successfully interact with, and learn from,

authority figures. Interactions with peers are also

important in each setting. Sometimes children

are forced to compete with peers for learning

resources (e.g., the teacher’s or coach’s attention),

whereas other times, peers take the role of team-

mates in exercises that require cooperation and

teamwork. EAs also provide children with an

opportunity to interact with more privileged peers,

who can model appropriate behavior in educa-

tional settings. We argue that the similar norma-

tive frameworks and social relations found in

classrooms and EAs promote similar types of non-

cognitive skills in children. The tasks required in

structured activities allow students to practice

e

d

c

b

a

SES

Race

SchoolAchievement

Noncognitive SkillsIn-School

Participation in Extracurricular

Activities

Figure 1. Conceptual model for understanding the relationship between extracurricular participationand educational outcomes.SES 5 socioeconomic status.

22 Sociology of Education 83(1)

noncognitive skills that are also valued by schools

(Gilman et al. 2004).6

Both school- and community-based EAs help

students develop their noncognitive skills through

opportunities to learn and use social and intellec-

tual skills, access to social networks of peers and

adults, and opportunities to face new challenges

(Eccles et al. 2003). EAs can help students to

work as a team and to practice interpersonal skills

as they work with others (Gilman et al. 2004).

Children who participate in sports and clubs are

seen by their teachers to exhibit better interper-

sonal skills than students who do not participate

in EAs (Fletcher et al. 2004). Participation in

EAs exposes participants to peers and adults

with important societal (including school) values

and a variety of skills (Gilman et al. 2004).

Students who participate in sports report higher

levels of work orientation and self-reliance

(Fletcher et al. 2003). If extracurricular participa-

tion has a positive effect on students’ noncogni-

tive skills, high-SES and nonminority students

would disproportionately benefit because of their

higher rates of participation in such programs.7

EAs, Noncognitive Skills, andAchievement

In our conceptual model, EAs have an indirect

relationship with achievement: EAs improve stu-

dents’ noncognitive skills, which are positively

related to academic achievement (Figure 1, line

c). Numerous studies have found a positive rela-

tionship between extracurricular participation

and academic achievement (see Broh 2002;

Fletcher et al. 2004; Guest and Schneider 2003;

Marsh and Kleitman 2002).8 Among the different

types of activities, sports activities consistently

have significant (and positive) effects on achieve-

ment, while the evidence on other activities is

more mixed (Broh 2002; Marsh 1992; Marsh

and Kleitman 2002; Steinberg 1996).

It is important to note that nearly all of the

research in this area focuses on middle and high

school students; virtually no research with nation-

ally representative data has examined the effects

of EAs on student achievement in elementary

school. This is an important omission, because

Lareau’s (2003) much cited study focused on ele-

mentary school students. Dumais (2006), using

Lareau’s theoretical framework, found that

students participating in sports, clubs, and dance

during either kindergarten or first grade had

greater reading gains (and math gains for dance

participants) between first and third grade com-

pared with students who did not participate in

EAs. In addition, she found that sports participants

were rated by teachers as having higher math

skills compared with students who did not

participate.

As Broh (2002) and Eccles et al. (2003) noted,

there is very little empirical evidence regarding

why EA have positive effects on learning out-

comes.9 Broh’s analyses indicate that ‘‘develop-

mental’’ variables (e.g., locus of control and

effort/homework time), along with stronger social

ties with adults, explained most of the positive

effect of sports on achievement.

We have decided to focus most heavily on

noncognitive skills as a possible meditating mech-

anism between EA and achievement in our concep-

tual model because prior research suggests that

peer relations and social ties outside the family

are less important for students’ outcomes among

elementary school students (e.g., Steinberg 1996).

Thus, in our analyses, we examine whether the aca-

demic benefits of EAs are attributable to stronger

work habits and engagement associated with

noncognitive skills. Numerous studies have indi-

cated that attitudes and behaviors associated with

students’ work habits and overall diligence are

consistently related to higher achievement (e.g.,

Carbonaro 2005; Farkas et al. 1990; Olneck and

Bills 1980; Rosenbaum 2001; Smerdon 1999).

However, very few studies of noncognitive skills

have focused on elementary school achievement,

and the results of such studies are somewhat mixed.

A recent study by Duncan et al. (2007) found that

attention skills predicted achievement, but ‘‘socioe-

motional’’ skills did not (net of other factors). In

contrast, Bodovski and Farkas (2008) found that

noncognitive skills are significantly and positively

related to reading gains in first grade. Thus, non-

cognitive skills are a highly plausible candidate

for explaining the EA-achievement relationship.

Variable Effect of EAs by SES

Our conceptual model shows how unequal partic-

ipation in EAs may contribute to the SES gap in

achievement: Higher rates of participation in EA

by high-SES students translate into enhanced

noncognitive skills that produce higher rates of

learning in the classroom. One key assumption


underlying this model is that all students benefit

equally from participation in EAs. Marsh and

Kleitman (2002) questioned this assumption.

Marsh (1992) argued that extracurricular partici-

pation helps students by increasing their commit-

ment to and identification with school. Because

high-SES students are already more likely to be

committed to and identify with school, Marsh

and Kleitman (2002) hypothesized that extracur-

ricular participation would disproportionately ben-

efit low-SES students.

Marsh and Kleitman (2002) focused on high

school students, but we argue that an EA-SES inter-

action for elementary students is also very plausible

(denoted by lines d and e in our model in Figure 1).

First, students’ home environments have greater ef-

fects on elementary school students because (1)

young children spend more time in the home and

less time with their peers, and (2) parents matter

more than peers for the outcomes of young children

(Steinberg 1996). Also, SES gaps in learning grow

more rapidly during the summer, when school is

not in session, which suggests that home environ-

ments are more unequal than school environments

(e.g., Alexander, Entwisle, and Olson 2001;

Downey et al., 2004). Finally, Lareau’s (2001,

2003) fieldwork indicates large SES differences in

parent-child interactions, expectations, and material

resources in children’s home environments (also

see Bianchi et al. 2004; Hart and Risley 1995, 1999).

Together, these findings suggest that low-SES

elementary school students will benefit more from

EAs than high-SES students. For low-SES chil-

dren, involvement in structured EAs replaces

unstructured activities and thereby provides

a new set of opportunities to learn and practice

the noncognitive skills valued by schools. The

extra opportunities provided by EAs may be

redundant for high-SES students, replacing expe-

riences and interactions in the home environment

that are already contributing to the development of

their noncognitive skills and academic learning.

Little research has examined whether the

effects of EAs vary by family background.

Marsh (1992) found that the positive effect of

EA on grades was larger for low-SES high school

students. However, Marsh and Kleitman (2002)

found that EAs generally had the same effects

on achievement for high- and low-SES high

school students. Dumais (2006) found that

lower-SES students’ reading gains benefit more

from participation in music lessons compared

with higher-SES students in music lessons. The

same pattern exists for art lessons and teachers’

ratings of students’ language art skills and for

music lessons and teachers’ ratings of math skills.

However, Dumais found that students from

higher-SES backgrounds participating in sports

rated higher on teachers’ ratings of math skills.

Overall, there is currently little research that

examines (1) the EA–SES interaction for elemen-

tary school students, especially for the age range

of the students in Lareau’s (2003) study, or (2)

noncognitive skills as an outcome for SES gaps.

Our analyses address both of these shortcomings

in prior research.

Reexamining the Sources of SESAdvantages: Research Questions

Much has been learned about SES inequalities and

the effects of EAs on student outcomes. However,

important questions about SES and extracurricular

participation remain unanswered, especially for

elementary school students. Our research ques-

tions address these important issues.

The first set of questions focuses on differen-

tial rates of participation in EA:

Research Question 1a: How much do students

from different SES backgrounds differ in

their participation in EAs?

Research Question 1b: Which aspects of fam-

ily background are most important in pre-

dicting EA participation?

Research Question 1c: Does the school context

affect students’ chances of participating in

EAs?

Our second and third questions focus on the rela-

tionship between extracurricular participation and

noncognitive skills in school. Past research and the-

orizing suggest that SES predicts student mastery of

the hidden curriculum in school (Farkas et al. 1990;

Farkas 2003; Lareau 2003).

Research Question 2a: Does extracurricular

participation affect students’ noncognitive

skills in the classroom?

Research Question 2b: Do these activities

explain part of the relationship between

SES and noncognitive skills?

Research Question 2c: Do EAs matter more

for the noncognitive skills of low-SES stu-

dents than high-SES students?


Finally, we are interested in how SES, EAs,

and noncognitive skills are linked to academic

outcomes for students. We argue that part of the

SES advantage in achievement works through

EA participation and its relationship with mastery

of the hidden curriculum in school.

Research Question 3a: Do EAs explain part of

the relationship between SES and academic

skills, and do noncognitive skills serve as

the mediating mechanism?

Research Question 3b: Do EAs contribute

more to the achievement gains of low-

SES students than high-SES students?

Together, these research questions will help us

better understand whether and how EAs serve as

an important source of advantage for students

from high-SES families.

DATA AND METHODS

The data set used for this study is the Early

Childhood Longitudinal Study–Kindergarten Class

of 1998–99 (ECLS-K; National Center for

Education Statistics 2004). ECLS-K is a nationally

representative sample of 21,260 children, and the

data focus on students’ early childhood experiences.

The third-grade wave was collected in the spring of

the students’ third-grade year, and it is the main

data source for our analysis.10

The third grade wave is ideal for our study for

several reasons. First, Lareau’s (2003) ethno-

graphic study followed fourth grade children,

recording their home environments and EA partic-

ipation. With the goal of examining the generaliz-

ability of Lareau’s findings in mind, we decided to

use nationally representative data from an age

group close to Lareau’s sample. Second, there

has been little sociological research examining

extracurricular participation in elementary school.

The current lack of sociological research is a gap

that should be filled, because students who have

not participated in sports or fine arts prior to

high school find it difficult to become involved

during high school (McNeal 1998). The participa-

tion in these activities in high school has been

linked positively to academic achievement and

to decreasing dropping out of school (Feldman

and Matjasko 2005).

Data collection in the ECLS-K was motivated

by a conceptual model that recognized the

importance of the interactions between the child,

family, and school (National Center for

Education Statistics 2004). A major strength of

the ECLS-K lies in its breadth: the study provides

information on participation in EAs, classroom

behavior, and test scores. ECLS-K also includes

data from parents and teachers about students,

as well as data on school characteristics.

Descriptive information for the variables used

in the analysis is presented in Table 1.

An additional strength of ECLS-K is the longi-

tudinal design of the study. This allows us to

account for students’ prior experiences, which

controls for students’ differing starting points.

The quasi-experimental design that we use is

one way to deal with selection bias by controlling

for variables that may be related to students par-

ticipating or not participating in EAs.

Dependent Variables

In our initial descriptive analyses, we use partici-

pation in EAs as a dependent variable to examine

participation differences. The EAs are primarily

independent variables and are described in detail

below.

Next, ‘‘approaches to learning’’ is our opera-

tionalization of noncognitive skills and is measured

by a scale that taps into characteristics of the stu-

dent’s attentiveness, organization, flexibility, task

persistence, learning independence, and eagerness

to learn (National Center for Education Statistics

2004). The items come from the Social Rating

Scale and are data reported by the teacher for

each student in the study. The approaches to learn-

ing scale is continuous, ranging from 1 to 4.11

Because one of the functions of school is to social-

ize students for later schooling and employment,

the approaches to learning measure allows us to

examine a set of noncognitive skills that is meant

to be an outcome of schooling and a benefit for

later life.

Although observational reports would be ideal

to measure classroom behavior, we believe that

teacher reports of classroom behavior are appro-

priate measures of classroom noncognitive skills.

We use teacher report measures from the spring

of the school year. By the spring, we believe

a teacher will have had adequate time to get to

know the students to make an accurate assessment

of the students (Madon et al. 1998). Moreover, the

approaches to learning scale focuses on concrete


behaviors rather than subjective judgments about

how ‘‘well behaved’’ the students are.

Finally, we use test scores as a measure of aca-

demic skills to gain a fuller understanding of the

Table 1. Means and Standard Deviations for Variables in the Analyses

Variable M SD Minimum Maximum

OutcomesApproaches to learning, G3 3.079 .673 1 4Third-grade reading

(n 5 10,047)109.729 19.458 42.42 148.95

Third-grade math(n 5 10,097)

86.217 17.396 31.05 120.42

EAsAny EA .816 — 0 1Sports .631 — 0 1Clubs .356 — 0 1Dance lessons .138 — 0 1Music lessons .206 — 0 1Art lessons .113 — 0 1Performing arts .247 — 0 1

Family backgroundFemale .496 — 0 1Black .106 — 0 1Hispanic .148 — 0 1Asian .052 — 0 1Other racea .053 — 0 1SES .037 .796 22.49 2.58Mom’s occupation prestige 33.838 21.208 0 77.5Dad’s occupation prestige 38.062 16.232 0 77.5Single-parent family .189 — 0 1Number of siblings 1.543 1.116 0 11Home activities scale 7.308 1.681 3.171 12.684Minutes of reading per week 80.970 85.974 0 420

Student academicApproaches to learning,spring G1

3.089 .684 1 4

Reading test, spring G1 69.691 20.350 16 141.36Math test, spring G1 56.261 15.785 9.12 107.42

School characteristicsPercentage free lunch 27.956 26.679 0 100Private .218 — 0 1Percentage minority

(n 5 10,811)\10% .366 — 0 110% to 25% .177 — 0 125% to 50% .160 — 0 150% to 75% .104 — 0 1.75% .193 — 0 1

Early Childhood Longitudinal Study–Kindergarten Cohort, third grade wave.EA 5 extracurricular activity; SES 5 socioeconomic status; G15first grade; G35third grade. Valid n 5 10,140 unlessotherwise noted.aThis category includes Native Hawaiians, other Pacific Islanders, Native Americans, Native Alaskans, and students ofmore than one race.


mediation of SES through EAs and noncognitive

skills. We conduct two separate regression analyses

on measures of academic skills: reading and math

IRT (item-response theory) test scores distributed

to the students in the spring of their third-grade

year. In the models that use test scores as the depen-

dent variable, approaches to learning becomes a pre-

dictor, mediating the relationship between EA

participation and achievement.

Independent Variables

Parents were asked if their children had partici-

pated in music lessons, dance lessons, performing

art activities, art lessons, sports, and clubs in the

past year outside of school hours. Responses to

each of the items were coded dichotomously,

with 1 indicating participation and 0 indicating

nonparticipation. These items served as our meas-

ures of extracurricular participation.

Parents could report that their children partici-

pated in more than one EA. We considered using

a variable that simply counted the number of activ-

ities children participated in, but such a measure

suffered from an important limitation: the use of

an additive scale would obscure possible differen-

ces in how specific EAs are related to specific stu-

dent outcomes. To avoid this problem, we used six

separate binary measures representing participation

in sports, clubs, dance lessons, music lessons, art

lessons, and performing arts activities.12

Although we are able to divide types of EAs

into six categories, the variables are not without

limitations.13 First, we know little about the dura-

tion or frequency of participation. We know that

a child participated in the activity within the

past year outside of the school day. This broad

time frame can result in a wide array of variation

in actual amounts of participation. One student

may be involved in a different sport each season,

resulting in hours spent in practices and games.

Another student may participate in baseball only

during the summer. Both of these students would

receive a 1 for the sports variable; however, we

are unable to determine differences in duration

and frequency effects. Another limitation of the

data is that we do not know under whose sponsor-

ship these activities take place. We do not know if

the activities are community based, school based,

summer programs, or private club activities. The

location of the activities may provide insight

into the program quality and the goals of the pro-

grams (Coakley 2007). Despite the limitations of

the EA variables, our study provides valuable

knowledge into the relationship among SES,

what occurs outside of school hours (i.e., EA),

and what occurs within the classroom (i.e., learn-

ing behaviors and academic skills).

The other key variable in the analysis is SES.

Our measure of SES is a household level compos-

ite of the father or male guardian’s education and

occupation, the mother or female guardian’s edu-

cation and occupation, and the household income,

which was compiled prior to the release of the

data (National Center for Education Statistics

2004).

Control Variables

To eliminate competing explanations for the rela-

tionship between EAs and approaches to learning,

it is important to control for other variables that

are related to extracurricular participation and

may affect a teacher’s evaluation of a student’s ap-

proaches to learning. First, the analyses control for

family structure (specifically, whether a child

comes from a single-parent family) and the number

of siblings in the family. When families have larger

numbers of children, the family resources are

reduced (Downey 1995), limiting the resources

available for EAs. A student’s home environment

may also affect his or her social and academic out-

comes. Parents who are more actively involved

with their children at home are also more likely

to involve them in EAs. Our goal was to separate

other features of concerted cultivation present in

the home from extracurricular participation. To

do this, we created a home activities scale using

factor analysis and a series of questions regarding

the home environment. The factor analysis indi-

cated three factors14: the factor for the home envi-

ronment, a factor for time spent reading to the child

(which is used to create a reading variable), and an

additional factor of other parenting practices

(which are not included in our models). The

home environment factor included five variables

clustering to suggest a factor statistically and sub-

stantively indicating parents and children perform-

ing activities together (a 5 .627). These activities

include helping the child do art, playing games,

teaching the child about nature, building ‘‘things,’’

and playing sports together.

In addition, the student’s gender and race are

controlled. Girls tend to be in structured activities

at this age of childhood more than boys (Fletcher

et al. 2003), and boys tend to have more freedom


in their choice of activities and freedom from

supervision (Posner and Vandell 1999). Gender

may also affect noncognitive skills. Posner and

Vandell’s (1999) results are consistent with past

research and found that during elementary school,

girls are more academically oriented than boys.

Regarding race/ethnicity, previous analyses using

the Early Childhood Longitudinal Study–

Kindergarten Cohort demonstrate that teachers

rate boys, African Americans, Hispanics, and stu-

dents from low-income families as having less

focused work habits compared with girls, whites,

and middle-class students (Denton and West

2002; Farkas 2003; Lee and Burkam 2002;

West, Denton, and Reaney 2001).

The child’s achievement level is controlled using

reading and math scores obtained during the student’s

first-grade year. Achievement levels may confound

the effects between extracurricular participation and

noncognitive skills. Those students who perform

well academically may be the students who are al-

lowed to participate in EAs and demonstrate the abil-

ity to stay on task, persist in their work, and be eager

to learn. By including prior test scores, we are able to

disentangle the relationship among prior cognitive

achievement, participation in EAs, and noncognitive

skills. Without including prior achievement, the rela-

tionship between prior achievement and noncogni-

tive skills may be attributed to participation in EAs,

confounding our results.

Finally, there are school factors that should

be controlled, such as school sector. Because

research shows differences between public and

private school students in terms of achievement

scores (Carbonaro 2006; Lubienski and Lubienski

2006), sector effects must be controlled to rule

out the possibility that the teacher assessments dif-

fer on the basis of the type of school. The SES level

of the school and surrounding community may pro-

vide insight into the resources available in the area,

which could include the extent and quality of EAs

offered. One limitation of these data, however, is

that it is unclear whether it is the school or the com-

munity that offers these activities. Nevertheless, the

percentage of students eligible for free lunch pro-

grams at school is used as a proxy measure for

the school’s SES level and is included in the anal-

yses as a control.

Missing Data

Multiple imputation (MI) was used to handle

the problem of missing data due to item

nonresponse.15 MI is an improvement over single

imputation and listwise deletion techniques

because it enables a researcher to maintain a large

sample size and not sacrifice statistical power in

the analyses (see von Hippel [2007] for more

about MI). Our analytic sample is limited by miss-

ing data. We first limited our sample to those

cases that contained information on our dependent

variable, approaches to learning, because we do

not use imputed values for the dependent variable

(von Hippel 2007). In addition, we limit our sam-

ple to students who were in both the spring first

grade and spring third-grade waves with parental

interviews completed. The sample was reduced

to those that had contained third grade test scores

when analyzing test scores as outcomes. In exam-

ining the extent of missing data of those with val-

ues on approaches to learning in third-grade, more

than three-fourths of the cases had no missing data

or had a missing value on only one variable.

Overall, most of the cases had few missing data.

The variables with the most missing data were

control variables, such as percentage free lunch

within the school. We used MI on our independent

and control variables to maintain a large sample

size.16

RESULTS

SES, Race/Ethnicity, andExtracurricular Participation

Our first research question focuses on how SES is

related to students’ levels of extracurricular partic-

ipation. Table 2 displays the bivariate relationship

between SES and participation in EAs. When

looking at the levels of extracurricular participa-

tion, all SES groups show high levels of extracur-

ricular participation: 60 percent of students whose

families are in the lowest SES quartile participate

in some sort of EA. Although this is a surprisingly

high rate of participation, we see that the percent-

age of students involved in EA steadily rises with

SES level: Participation rates jump to 80.6 percent

for the next quartile, and the highest two quartiles

enjoy near universal participation (90 percent and

95.2 percent). This general pattern of relative SES

differences is consistent across activity types:

higher SES students are more likely to participate

in every type of EA examined in this study (results

available on request). For each SES quartile,

sports is the most popular EA. In short, there is

clear evidence of differences in participation by


SES, but substantial numbers of low-SES students

are still very active in EAs. The high percentage

of participation in EAs in general is not surprising

because of recent increases in participation due to

governmental funding for after-school programs,

the rise in the number of mothers at work, and

research results finding positive outcomes for par-

ticipation (Mahoney et al. 2006).

Examining the percentages of students who par-

ticipate in EAs by parental education level tells

roughly the same story (results available on

request). As parental education increases, the pro-

portion of students who participate in any EA for

each level of parental education increases.

Roughly half of the students whose parents have

less than a high school education participate in

EAs, compared with almost 70 percent of students

with parents who have high school diplomas or

the equivalent. Remarkably, nearly all (94.8 per-

cent) of the students in the mostly highly educated

group (master’s degrees or higher) participated in

some sort of EA. Once again, sports has the highest

participation level in all of the parental educational

categories, from a low of 27.8 percent of those stu-

dents whose parents have less than a high school

diploma to a high of 80.2 percent of those students

whose parents have advanced degrees.

Another component of SES that we examine is

income. The results are generally consistent with

the previous two measures discussed above.

Overall, families at the higher end of the income

distribution have higher percentages of students

participating in an EA. Differences in participa-

tion are more pronounced at the high and low

ends of the income distribution than is the case

in the middle. However, although children from

low-income families are the least likely to partic-

ipate in EAs, it should also be noted that most of

these children do participate in some type of EA

outside of school. Finally, students whose parents

have higher levels of occupational status are more

likely to participate in each type of EA that we

examined (results available upon request).

Overall, the findings are supportive of

Lareau’s (2003) claim that SES is related to extra-

curricular participation. However, the findings

also indicate that many children in low-SES fam-

ilies (both low income and less educated) partici-

pate in EAs, many more than one would guess

from Lareau’s study. Lareau also concluded that

race differences in participation reflect nothing

more than SES differences between white and

black families. Table 2 also provides findings on

racial-ethnic differences in extracurricular partici-

pation. Overall, whites are the most likely to par-

ticipate in EAs (87.7 percent) compared with

blacks (72.4 percent), Asians (75.3 percent),

Hispanics (66.1 percent), and students of other

races (76.9 percent). When examining racial dif-

ferences in sports (results available on request),

the pattern becomes even more pronounced: 72

percent of white students participate in sports of

some kind, substantially more than the 46 percent

of black students. White students have a higher

percentage of participation compared with black

students in every type of activity except perform-

ing arts. These results are consistent with the

pattern of racial differences in extracurricular

participation documented in Dumais’s (2006)

examination of kindergarten and/or first-grade

extracurricular participation.

Because race/ethnicity, income, education, and

occupational status are all strongly correlated, we

present a multivariate analysis to determine

whether each of these variables is independently

related to extracurricular participation, net of the

others. Table 3 presents the results of a logistic

regression with participation in any EA as the

dependent variable. The first two models examine

the relationship between SES and extracurricular

participation. The first model includes the bivari-

ate relationship between SES and extracurricular

participation. Not surprisingly, the relationship is

positive and significant. In the next model, we dis-

aggregate our SES measures into income, parental

education, and occupational status to examine

whether these characteristics are independently

Table 2. Means of Participation in EAs by SES andRace

Variable n Any EA

SES quartileLow 2,496 .605Second 2,571 .806Third 2,524 .900High 2,549 .952

RaceWhite 6,496 .877Black 1,073 .724Hispanic 1,504 .661Asian 526 .753Other 541 .769

EA 5 extracurricular activity; SES 5 socioeconomicstatus.


related to extracurricular participation. As model

2 indicates, all three components of SES are inde-

pendently related to extracurricular participation.

Thus, the findings are consistent with both

Lareau’s (2003) hypothesis about the importance

of ‘‘cultural repertoires’’ and the competing

hypothesis regarding constraints on extracurricu-

lar participation due to insufficient income.

Model 3 examines whether racial-ethnic differ-

ences in extracurricular participation reflect SES

differences (as Lareau [2003] claimed). Net of

SES, white students are still more likely to partic-

ipate in EAs compared with all other races, sug-

gesting that participation in EAs is not simply

about SES but also about race. Model 4 includes

a measure of the percentage minority of the school

that the student attends. We use this measure as

a proxy for the racial segregation that the child

is exposed to and the resulting differences

in opportunities structures. When the racial

Table 3. Relationship between SES, Race, and Extracurricular Activity Participation (standardizedcoefficients)

Variable Model 1 Model 2 Model 3 Model 4

SES .447*** .403*** .393***(.015) (.015) (.015)

Income .064***(.005)

Parental educationHigh school or equivalent .187***

(.034)Vocational/technical/some .336***

college (.035)College/some graduate school .505***

(.044)MA/MS/PhD/professional

degree.584***

(.057)Mom’s occupation prestige .002***

(.0005)Dad’s occupation prestige .002*

(.0009)Black 2.099*** 2.024

(.028) (.034)Asian 2.240*** 2.183***

(.039) (.042)Hispanic 2.226*** 2.159***

(.024) (.029)Other 2.130* 2.090*

(.039) (.041)Percentage minority

10% to 25% .048(.031)

25% to 50% 2.048(.030)

50% to 75% 2.071*(.034)

.75% 2.119***(.033)

SES 5 socioeconomic status. N 5 10,140 (except for model 4, for which n 5 9,988). Values in parentheses arestandard errors. The dependent variable was participation in any extracurricular activity.*p \ .05. **p \ .01. ***p \ .001.


composition of the school is added, we find that

black students are not significantly less likely to

participate in EAs. However, Asian, Hispanic,

and other race students are still less likely than

white students to participation in some form of

EAs.

To make these results more easily interpretable,

Figure 2 displays the predicted probabilities from

the logistic regression presented in Table 3, model

4. When SES is held to the mean in a school with

less than 10 percent minority students, the pre-

dicted probability of a white student’s participating

in any EA is 88.2 percent. Although all of the

predicted probabilities at this level of SES and

percentage minority are above 81 percent, white

students still have a higher probability of participa-

tion. Even when the SES values are set to a standard

deviation above and below the mean, the same

pattern is observed: white students have higher

predicted probabilities than other racial groups

with the same value for SES. No matter the racial

composition of the school, white students are the

most likely to participate in EAs. However, as the

percentage minority within the school increases,

all students are less likely to be in an EA. Thus,

our findings do not support Lareau’s (2003) conclu-

sion that SES accounts for racial-ethnic differences

in extracurricular participation, but racial segrega-

tion does account for the black-white differences

in participation. There is no main effect for black

students once school segregation is included, sug-

gesting that black students’ likelihood of EA partic-

ipation depends on the schools they attend.

Because black students are more likely to attend

high minority schools, the school context appears

to be the main source of unequal participation

between black and white students.

To examine the role of race on separate activ-

ities, we ran logistic regressions (models 2 and 4

in Table 3) for each specific activity (see Table

4). The results for participation in sports and clubs

mirror the results for participation in any EA: as

income, education, and occupational prestige

increase, students are more likely to participate.

In addition, net of SES and school racial composi-

tion, white students are still more likely to partic-

ipate in sports and clubs compared with other

racial groups, with the exception of no significant

difference for participation in clubs between black

and white children. This pattern is less consistent

for the other four activities.

Interestingly, we find that as the percentage

minority within the school increases, so does a stu-

dent’s likelihood of participating in fine art EAs.

On the other hand, students in high-minority

schools are less likely to participate in sports

and clubs. Net of the student’s SES, the percent-

age minority within the school is related to the

opportunities that students have to participate in

certain types of EAs.

Figure 2. Predicted probabilities of extracurricular participation by socioeconomic status (SES), race,and school percentage minority.SD5standard deviation


We find, as Lareau (2003) suggests, that stu-

dents from high-SES families are more likely to

participate in EAs. In contrast to Lareau, we

find that race is related to extracurricular partici-

pation. With a few exceptions, white students

are more likely to be in EAs net of social class.

Unequal rates of participation in EA are not

explained completely by SES.

Does Extracurricular ParticipationExplain SES Differences inNoncognitive Skills in School?

Our next set of analyses examines the relation-

ships between extracurricular participation, SES,

and noncognitive skills. Model 1 (see Table 5)

examines whether involvement in specific EAs

(sports, clubs, dance, music, art, and performing

arts) are related to noncognitive skills in school.17

These findings indicate that five of the six cate-

gories of EAs have a positive and significant

relationship with approaches to learning; how-

ever, the coefficients vary in magnitude and

significance. Dance displays the strongest net

relationship, followed by music, sports, clubs,

and finally performing arts (which is marginally

significant). The second model examines the

relationships between SES, other background

characteristics, and noncognitive skills. Family

SES has a substantial, significant association

with in-school approaches to learning.

The third model combines EAs and family

background characteristics into the same model.

As the findings indicate, background characteris-

tics explain much of the association between

EAs and noncognitive skills in school. Clubs

and performing arts are no longer significant,

and the dance and music coefficients are much

reduced from model 1. Sports remains significant

and is reduced the least of any of the other activ-

ities. The magnitude of SES is reduced by about

17 percent, suggesting that participation in EAs

explains a modest portion of the relationship

between SES and noncognitive skills.

Model 4, our fully adjusted model, controls for

additional student and school variables that may be

related to an increased mastery of noncognitive

Table 4. Summary Table of Separate Logistic Regression Predicting Participation

Variable Any EA Sports Clubs Dance Music Art Performing Arts

Model 2Income 1*** 1*** 1*** 1*** 1*** 1 1

High school/equivalent 1*** 1*** 1*** 2 1* 1 1

Vocational/technical/somecollege

1*** 1*** 1*** 1 1*** 1 1***

College/some graduate school 1*** 1*** 1*** 1** 1*** 1** 1***MA/MS/PhD/professional

degree1*** 1*** 1*** 1** 1*** 1*** 1***

Mom’s occupation prestige 1*** 1*** 1* 2 2 1 1

Dad’s occupation prestige 1* 1* 1* 2 1* 2 2

Model 4SES 1*** 1*** 1*** 1*** 1*** 1*** 1***Black 2 2*** 2 2 1** 2 1***Asian 2*** 2*** 2*** 1 1*** 1 1

Hispanic 2*** 2*** 2*** 1* 1 2 2**Other 2* 2** 2* 1 1* 1* 1

Percentage minority10% to 25% 1 2 1 1 1 1* 1*25% to 50% 2 2*** 2* 1* 1 1** 1**50% to 75% 2* 2*** 2*** 1** 2 1 1**.75% 2*** 2*** 2*** 1*** 2 1* 1**

EA 5 extracurricular activity; SES 5 socioeconomic status. N 5 10,140. Standardized coefficients are omitted fromthe table for the sake of simplicity and are available on request.*p \ .05. **p \ .01. ***p \ .001.


Table 5. Relationship between EAs and Approaches to Learning

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

EAsSports .124*** .106*** .060*** .062*** .102***

(.014) (.014) (.012) (.012) (.015)Clubs .050*** 2.012 2.012 2.011 2.012

(.014) (.014) (.012) (.012) (.012)Dance .213*** .066** .062*** .069*** .070***

(.020) (.020) (.017) (.018) (.018)Music .159*** .055** .001 .010 .011

(.017) (.017) (.014) (.016) (.016)Art .020 2.008 2.005 .004 .006

(.021) (.020) (.018) (.018) (.018)Performing arts .036* .015 2.002 2.002 2.002

(.017) (.016) (.014) (.014) (.014)Family background

SES .166*** .138*** .037*** .020 .032*(.009) (.009) (.009) (.015) (.015)

Black 2.173*** 2.166*** 2.058** 2.058** 2.003(.022) (.022) (.020) (.020) (.028)

Hispanic 2.007 .003 .037* .033 .112***(.019) (.019) (.017) (.017) (.024)

Asian .224*** .233*** .185*** .187*** .252***(.028) (.029) (.026) (.026) (.035)

Other race 2.048 2.042 .023 .023 .031(.028) (.028) (.025) (.025) (.025)

Student academicApproaches to learning, spring

first grade.365*** .364*** .364***

(.010) (.010) (.010)Test score average, spring first

grade.011*** .011*** .011***

(.0006) (.0006) (.0006)School characteristics

Percentage free lunch .001*** .001*** .001**(.0003) (.0003) (.0003)

Private .036* .035* .035*(.015) (.015) (.015)

Interaction termsSports 3 SES .056*** .038*

(.016) (.016)Clubs 3 SES 2.012 2.012

(.016) (.016)Dance 3 SES 2.032 2.030

(.021) (.021)Music 3 SES 2.026 2.024

(.018) (.018)Art 3 SES 2.043* 2.045*

(.021) (.021)Performing Arts 3 SES .003 .002

(.018) (.017)Sports 3 Black 2.093*


skills. We find that students’ previous academic

and noncognitive skills are related to their later

noncognitive skills: students with higher levels of

prior achievement have greater mastery of noncog-

nitive skills in third grade. In addition, students in

private schools have higher levels of noncognitive

skills, on average. Surprisingly, as the percentage

free lunch increases in a school, a student’s non-

cognitive skills increase. From models 1 to 4, we

see that although participation in EAs mediates

a modest portion of the relationship between SES

and noncognitive skills, the inclusion of student

characteristics and school characteristics explains

most of the association between SES and the mas-

tery of noncognitive skills.

Model 5 includes interaction terms for each of

the separate EAs and SES to allow the relationship

between participation in specific EAs and ap-

proaches to learning to vary by SES. The results

of this model indicate that there is a significant

interaction between sports participation and SES,

but the direction of the relationship is the opposite

of what is hypothesized. Higher-SES students ben-

efit more from sports participation than students

from lower-SES families. The predicted value of

noncognitive skills that a student from a family

with an SES level one standard deviation above

the mean (average on all other characteristics)

who participates in sports is 3.155. This value is sta-

tistically significantly different from the same high-

SES student who does not participate in sports

(3.048). The high-SES sports-participating student

receives an additional boost from participating in

sports. However, there is not a statistically

significant difference in noncognitive skills between

a low-SES student (with the same average charac-

teristics as the hypothetical high-SES student) who

participates in sports (3.037) and one who does not

(3.017). The interaction effect suggests that the ben-

efit from sports participation works differently de-

pending on a student’s SES level.

The interaction effects also indicate that not all

EAs have the same relationship with noncognitive

skills. Most of the interactions are insignificant,

thereby indicating that those EAs have the same

relationship with noncognitive skills for students

with different family backgrounds. The interaction

between art lessons and SES is marginally signif-

icant, and unlike the sports-SES interaction, the

sign matches our expectation: low-SES students

are more affected by art lessons than are high-

SES students

Above, we noted that race is related to participa-

tion in EAs. In addition to examining whether the

relationship between participation and noncognitive

skills varies by SES, we also examine whether that

relationship varies by racial group (see model 6).18

There are negative interaction effects for sports

and black, Asian, and Hispanic. It is somewhat

difficult to summarize the overall pattern of

race-participation interactions. To have a fuller

understand of the racial interactions, we include

predicted values19 for a Hispanic student who

participates in sports and a Hispanic student

with the same average characteristics who does

not participate in sports. We use a Hispanic stu-

dent as our example because it is the only racial

interaction that is more than marginally

Table 5. Continued

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

(.037)Sports 3 Asian 2.115*

(.050)Sports 3 Hispanic 2.143***

(.033)Constant 2.910*** 2.924*** 2.848*** 1.120*** 1.119*** 1.096***

(0.012) (0.031) (0.033) (0.043) (0.043) (0.044)Adjusted R2 .0410 .1341 .1413 .3519 .3529 .3544

N 5 10,140. EA 5 extracurricular activity; SES 5 socioeconomic status. Values in parentheses are standard errors.Control variables omitted from the table but included in the models 2 to 6 were female, single-parent family, numberof siblings, home activities scale, and minutes of reading per week. Stata does not report the adjusted R2 value usingthe micombine reg command. Therefore, we report the adjusted R2 value for one of the data sets.*p \ .05. **p \ .01. ***p \ .001.


significant. A Hispanic student who participates

in sports and is average on other characteristics

has a predicted approaches to learning value of

3.073, whereas if the same Hispanic student

were to not participate, he or she would have

a value of 3.111 on approaches to learning. A

Hispanic student who participates in sports is dis-

advantaged by the participation. A white student

(average on all other characteristics) who partic-

ipates in sports would have a predicted noncogni-

tive skills value of 3.103. However, if that white

student did not participate in sports, he or she

would have a value of 2.996. Black students par-

ticipating in sports are at a disadvantage com-

pared with whites participating in sports. Asians

enjoy an advantage compared with whites in

sports or not in sports. Yet it is Asians who do

not participate in sports who have higher ratings

of noncognitive skills. Although there are

statistically significant interactions, the benefit

from not participating in sports for an average

student who is Hispanic is substantively small

compared with if that student were to participate

in sports.

Does Extracurricular ParticipationExplain SES Differences in AcademicSkills?

Our final set of analyses examines the link

between SES and academic outcomes: How are

SES, EAs, and noncognitive skills related to aca-

demic outcomes? Tables 6a and 6b provide ordi-

nary least squares regression results using the

third grade reading and math test scores as the

dependent variables.

The unadjusted model reveals that participation

in sports, clubs, dance, music, art, and performing

arts is significantly and positively related to an

increase in reading test scores (see Table 6a). We

argue that the relationship between SES and aca-

demic skills is mediated by participation in EAs

and noncognitive skills. Model 2 is our baseline

model, with which we compare subsequent models

with mediators. The SES relationship with achieve-

ment should decrease as we include our mediators

to the model. In model 2, we find that SES is

strongly related to achievement in our unadjusted

background model. A one-unit increase in SES

leads to over a nine-point increase in academic

skills. In addition, white students have higher test

scores compared with every other racial category.

Once again, model 3 includes both participation

in EAs and background characteristics. Much of

the relationship between EAs and academic skills

is explained by background characteristics. The

EAs that remain significant stay at the same level

of significance, with sports, clubs, music, and per-

forming arts remaining significant, but the magni-

tudes are reduced compared to model 1. Model 3

also finds that participation in EAs does not explain

a large portion (11 percent) of the relationship

between SES and academic skills.

We theorized that noncognitive skills mediate

the relationships of SES and EAs with academic

skills. These relationships should be reduced

with the inclusion of noncognitive skills in the

model. Model 4 shows that the addition of non-

cognitive skills does not reduce much of the mag-

nitude for SES, clubs, music, or performing arts.

The coefficient for SES is reduced by 16 percent

and still provides a sizable advantage for reading

scores. However, the inclusion of noncognitive

skills does mediate much of the relationship

between sports participation and reading skills

(63 percent). This is consistent with our argument

that the academic benefits of extracurricular par-

ticipation are largely explained by improvement

in students’ noncognitive skills in the classroom.

Like model 4 of Table 5, model 5 is our full

model that controls for factors other than our

key independent variables. By including these

additional variables, such as prior test scores, we

are able to understand more fully what the direct

relationships between our independent variables

and test scores are. We find that much of the

coefficients for SES, clubs, music, and noncognitive

skills are explained by including our additional con-

trols. The full model tells us that the inclusion of

prior test scores and sector explains more of the

relationships between SES and participation in

EAs and reading skills than noncognitive skills do.

We repeated the above regressions using third

grade math test scores as the dependent variable

(See Table 6b). We find similar results between

reading and math test scores, including the signif-

icance of sports, clubs, and music participation in

relation to a student’s math scores in model 1.

Once again, model 2 shows that SES is strongly

related to test scores, specifically math scores. In

addition when the unadjusted models are com-

bined, the inclusion of background variables ex-

plains a sizable (70 percent) portion of the

coefficient for sports, whereas only 12 percent

of the coefficient for SES is explained by the


Table 6a. Relationship between EAs and Approaches to Learning with Third-Grade Reading

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

EAsSports 6.478*** 1.650*** 0.612 20.314 20.452

(0.391) (0.378) (0.354) (0.280) (0.282)Clubs 5.267*** 1.878*** 2.026*** 1.361*** 1.329***

(0.399) (0.367) (0.342) (0.270) (0.277)Dance 2.199*** 0.038 20.607 20.028 20.175

(0.573) (0.535) (0.500) (0.393) (0.404)Music 6.996*** 3.006*** 2.504*** 0.847* 1.147**

(0.477) (0.441) (0.412) (0.328) (0.358)Art 1.402* 0.787 0.852 0.523 0.670

(0.600) (0.542) (0.506) (0.398) (0.417)Performing arts 1.207* 1.052* 0.906* 0.217 0.217

(0.471) (0.428) (0.400) (0.314) (0.321)Family background

SES 9.312*** 8.249*** 6.952*** 2.418*** 3.386***(0.229) (0.247) (0.233) (0.204) (0.332)

Black 29.284*** 29.251*** 27.654*** 23.450*** 23.481***(0.593) (0.596) (0.558) (0.471) (0.472)

Hispanic 25.784*** 25.342*** 25.350*** 21.864*** 21.805***(0.504) (0.507) (0.473) (0.398) (0.397)

Asian 22.322** 22.122** 24.351*** 23.800*** 23.810***(0.762) (0.770) (0.721) (0.574) (0.574)

Other race 26.799*** 26.643*** 26.220*** 23.206*** 23.250***(0.760) (0.759) (0.708) (0.576) (0.575)

Student academicReading test, spring first grade 0.396*** 0.396***

(0.008) (0.008)Math test, spring first grade 0.282*** 0.282***

(0.011) (0.011)Third grade approaches to

learning9.651*** 4.176*** 4.175***

(0.250) (0.210) (0.210)School characteristics

Percentage free lunch 20.060*** 20.058***(0.007) (0.007)

Private 20.794* 20.776*(0.343) (0.343)

Interaction termsSports 3 SES 21.072**

(0.351)Clubs 3 SES 0.089

(0.345)Dance 3 SES 0.322

(0.471)Music 3 SES 20.892*

(0.402)Art 3 SES 20.677

(0.475)Performing Arts 3 SES 20.020

(0.392)

(continued)


Table 6a. Continued


Constant 101.536*** 114.855*** 112.774*** 85.263*** 56.899*** 57.129***(0.338) (0.849) (0.891) (1.095) (0.978) (0.980)

Adjusted R2 .0904 .2589 .2680 .3627 .6069 .6074

EA 5 extracurricular activity; SES 5 socioeconomic status. N 5 10,047. Values in parentheses are standard errors.Control variables omitted from the table but included in the regression were female, single-parent family, number ofsiblings, home activities scale, and minutes of reading per week. Stata does not report the adjusted R2 value using themicombine reg command. Therefore, we report the adjusted R2 value or one of the data sets.*p \ .05. **p \ .01. ***p \ .001.

Table 6b. Relationship between EAs and Approaches to Learning with Third-Grade Math


EAsSports 7.277*** 2.207*** 1.251*** 0.216 0.096

(0.351) (0.343) (0.319) (0.229) (0.231)Clubs 3.765*** 1.432*** 1.562*** 0.826*** 0.868***

(0.359) (0.334) (0.311) (0.222) (0.228)Dance 20.014 0.362 20.244 0.160 0.111

(0.514) (0.486) (0.451) (0.324) (0.333)Music 5.259*** 2.257*** 1.776*** 0.423 0.405

(0.428) (0.401) (0.372) (0.269) (0.300)Art 0.679 0.468 0.541 0.332 0.290

(0.535) (0.488) (0.454) (0.326) (0.341)Performing arts 20.462 0.278 0.143 20.284 20.210

(0.422) (0.387) (0.360) (0.258) (0.263)Family background

SES 7.639*** 6.712*** 5.472*** 1.679*** 2.562***(0.207) (0.224) (0.210) (0.166) (0.270)

Black 210.940***210.716***29.187***24.229***24.299***(0.535) (0.538) (0.501) (0.378) (0.378)

Hispanic 24.344*** 23.899***23.922***20.510 20.463(0.456) (0.459) (0.426) (0.318) (0.319)

Asian 20.227 0.146 21.966** 20.155 21.031(0.693) (0.700) (0.652) (0.472) (0.584)

Other race 26.496*** 26.237***25.824***22.013***22.072***(0.688) (0.687) (0.638) (0.463) (0.463)

Student academicReading test, spring first grade 0.130*** 0.130***

(0.007) (0.007)Math test, spring first grade 0.624*** 0.624***

(0.009) (0.009)Third-grade approaches to

learning9.038*** 3.456*** 3.463***

(0.225) (0.172) (0.173)School characteristics

Percentage free lunch 20.028***20.026***(0.005) (0.005)


EA variables. When we add our measure of non-

cognitive skills to the model, the SES coefficient

is reduced by 18 percent and music by 21 percent,

whereas the coefficient for sports is reduced by 43

percent. A student’s noncognitive skills explains

a larger share of the relationship between sports

and math scores compared with SES or music

and math scores. As is the case with reading, the

inclusion of prior test scores and additional varia-

bles in our full model explains most of the relation-

ship between SES, extracurricular participation,

noncognitive skills, and math scores.

Variable Effects of ExtracurricularParticipation on Achievement by SES

Our last models explore whether the relationship

between EAs and academic achievement varies by

SES level. Model 6 in Tables 6a and 6b indicates sig-

nificant negative interactions between sports partic-

ipation and SES: in substantive terms, SES is more

weakly related to achievement for students who par-

ticipate in sports than for those who do not. To inter-

pret the interaction effects more easily, we calculated

predicted values for a student average on all charac-

teristics except sports participation and SES and

plotted them on a graph (see Figure 3). Overall, an

average student who is high SES (measured as one

standard deviation above the mean) and participates

in sports has a lower reading score than a high-SES

student who did not participate in sports (111.52

vs. 112.83). For low-SES students, sports participa-

tion has a very small benefit for reading achieve-

ment. In math, a low-SES student (average on all

other characteristics) who participates in sports

(85.026) has a higher math score compared with if

that student did not participate in sports (84.227).

Thus, the average student who is low SES does ben-

efit from participating in sports for his or her math

achievement. As with reading, high-SES students

who do not participate in sports have higher math

achievement than those who do, but the difference

is fairly small.

Table 6b. Continued


Private 22.968***22.946***(0.275) (0.275)

Interaction termsSports 3 SES 20.909**

(0.289)Clubs 3 SES 20.221

(0.286)Dance 3 SES 0.047

(0.388)Music 3 SES 20.402

(0.331)Art 3 SES 0.052

(0.391)Performing Arts 3 SES 20.415

(0.323)Music 3 Asian 2.427*

(0.965)Constant 79.234*** 92.500*** 90.272*** 64.538*** 35.279*** 35.493***

(0.303) (0.761) (0.802) (0.981) (0.791) (0.792)Adjusted R2 .0810 .2361 .2445 .3489 .6661 .6666

EA 5 extracurricular activity; SES 5 socioeconomic status. N 5 10,047. Values in parentheses are standard errors.Control variables omitted from the table but included in the regression were female, single-parent family, number ofsiblings, home activities scale, and minutes of reading per week. Stata does not report the adjusted R2 value using themicombine reg command. Therefore, we report the adjusted R2 value or one of the data sets.**p \ .01. ***p \ .001.


We expected that low-SES students would

benefit more from participating in EAs because

they do not have the same opportunities in their

home environment as high-SES students. The re-

sults of model 6 in Tables 6a and 6b are consistent

with the direction that we originally hypothesized,

yet it is unclear as to why the pattern in the non-

cognitive skills model differs from both our

hypothesis and the achievement score models.

We will discuss the SES interactions in more

detail in the following section. The only signifi-

cant, and marginal at that, race and EA interaction

is between Asian and music for math test scores.

Asians who participate in music lessons are

advantaged in terms of math scores compared

with Asian students who do not take music les-

sons, white students taking music lessons, and

white students not taking music lessons.

DISCUSSION: EAs AND SESADVANTAGES

We examined whether participation in EAs serves

as a source of advantage for students from high-

SES families. Our study complements and extends

Lareau’s (2003) Unequal Childhoods by analyzing

data from a nationally representative sample with

multivariate methods that explore the relationship

between EAs, social class, and academic outcomes.

Moreover, we hypothesized that noncognitive skills

would act as a key mediating mechanism that trans-

lated SES advantages in EAs into academic gains

for high-SES students. Overall, we find partial sup-

port for our conceptual model.

The first set of research questions focused on

differential rates of participation in EA. Our

findings indicate that education level, income, and

occupational prestige are related to higher levels

of participation in EA, which is consistent with

Lareau (2003) and our conceptual model.

However, our results also expand on Lareau’s find-

ings. In Lareau’s ethnographic study, she examined

the levels of EA participation among a selected

group of students, finding that children from

higher-SES families participate more in structured

activities compared with poor and working-class

families. We find that participation levels are high

among third graders from all SES levels.

However, we do see that in general, as measures

of SES increase, so do rates of participation in

EAs, reaching near saturation points at the highest

level. This finding is consistent with Chin and

Phillips (2004), who found that parents from all

social classes wanted their children to participate

in summer camps, but lower-SES families experi-

enced constraints that prevented their children

from participating. Yet SES is not the only dimen-

sion on which participation in EA varies. In contrast

with the results from Lareau’s ethnographic study,

white students are more likely to participate in EA

compared with the other racial and ethnic groups,

and this holds at one standard deviation above and

below the mean and mean levels of SES in the cur-

rent sample. However, when disaggregating the

activities, white students are less likely to partici-

pate in dance lessons, music lessons, art lessons,

and performing arts activities, net of SES, com-

pared with other racial groups. Thus, participation

in EAs is related to race as well as SES.

The different levels of extracurricular participa-

tion between black and white students is explained

largely by the racial composition of the school,

Figure 3. Predicted test score values by sports participation and socioeconomic status (SES).SD 5 standard deviation


which we use as a proxy for the student’s expo-

sure to opportunities to participate in EAs.

Continued school and neighborhood segregation

in the United States creates different extracurric-

ular opportunity structures for students, which

drive unequal access and rates of participation.

Moreover, black families tend to have tighter con-

nections to extended families compared with white

families (Gosa and Alexander 2007). Combined

with limited access to opportunities to participa-

tion in EAs, black children may be more likely to

spend time with cousins and fictive kin.

Although Lareau (2003) described lower- and

working-class families’ spending more time with

extended family compared with upper-class fami-

lies, the same process may be working for black

families, which is a characteristic of accomplish-

ment of natural growth. Finally, our finding of

increased participation in fine art programs in

high-minority schools could be due to community

projects targeted at maintaining music and art in

areas that no longer have the programs within the

schools. The higher participation of black children

in fine art activities may also be related to the

emerging black middle class, which has emerged

more recently than the white middle class (Gosa

and Alexander 2007). Black middle-class parents

may encourage their children to participate in

fine art activities as part of developing middle-

class tastes and dispositions. However, it may

take generations for the influence of the black mid-

dle-class parenting approaches to manifest in

achievement gains (Phillips et al. 1998).

Our second set of research questions examined

how EAs were related to the development of noncog-

nitive skills. We found that some EAs are related to

an increase in noncognitive skills, particularly par-

ticipation in sports and dance. This suggests that

there is a connection between how students spend

their leisure time and their school performance.

EAs provide students with an opportunity to interact

with authority figures and privileged peers, provid-

ing them with access to important noncognitive skills

that facilitate academic learning. The similar context

between EAs and the classroom helps students prac-

tice skills that are valued within the classroom set-

ting. However, we found that participation in EAs

does not mediate much of the SES effect on noncog-

nitive skills. SES continues to have a direct relation-

ship on noncognitive skills net of other family

resources and behaviors.

Our third set of research questions examined

whether EAs affected achievement outcomes and

whether this relationship was explained by differen-

ces in noncognitive skills. Much of the relationship

between EAs and achievement is explained by dif-

ferences in noncognitive skills. This is an important

contribution of our study because little is known

about why extracurricular matter for achievement,

especially for children in elementary school.

However, EAs explained only a small amount of

the SES advantage in achievement. One reason

EAs did not explain more of the SES effect on

achievement may be that the differences in EA par-

ticipation by SES may be too small to be an impor-

tant mediator. As already noted, a majority of low-

SES students are participating in EAs, and this likely

constrains how much of the SES-achievement rela-

tionship can be explained by EA participation.

Finally, we examined whether EAs had different

effects on outcomes for high- and low-SES students.

We predicted that the relationship between EAs and

student outcomes would vary by SES level, with

low-SES students benefiting more than high-SES

students.20 Our results are only partly consistent

with this prediction. When examining the relation-

ship of sports participation and noncognitive skills,

students from higher SES families enjoy an addi-

tional benefit from participation. In terms of predict-

ing academic skills, our interactions are in the

direction we predicted. Our mixed findings are con-

sistent with those of Dumais (2006), who found that

higher SES students who participate in sports score

higher on teachers’ evaluations of math skills, yet

participation in art and music lessons provided

low-SES students an added advantage over high-

SES students for teachers’ evaluations of language

art skills and actual reading gains.

In our theory, we suggest that EAs provide

a location, in addition to home, where high-SES

students learn noncognitive skills. When predict-

ing noncognitive skills, higher-SES students bene-

fit from sports because there is reinforcement of

noncognitive skills among home, school, and

EAs. Children are able to practice and receive

reinforcement for their noncognitive skills in mul-

tiple contexts. Moreover, parents with high SES

may have greater engagement with the EAs by

observing their children participating in the EAs

and may comment on their children’s behavior

during the EAs. The reinforcement of noncognitive

skills in the home may explain why high-SES

children benefit more from sports participation

compared with low-SES children.

However, the SES-EA interactions for our

achievement models are consistent with our


hypotheses. On closer examination of our predicted

values of achievement scores, we find that high-

SES students who participate in sports have lower

reading scores compared with high-SES students

who do not participate. In our math models, low-

SES students who participate in sports do have

higher test scores compared with low-SES students

who do not participate. We find that our theory for

interactions aligns best with the math scores. Low-

SES students receive a boost in math from partici-

pating in sports that their sport-non-participating

peers do not receive. Math scores tend to be less

connected to the home environment compared

with reading scores. Participation in sports allows

low-SES students to interact with adults and peers

to learn and practice noncognitive skills that facili-

tate learning within the classroom. High-SES stu-

dents who participate in sports do not receive an

added math boost because sports are an additional

context rather than a compensatory context in

which to practice noncognitive skills. For reading,

the home environment is an important element to

consider. High-SES students who participate in

sport activities will have less time to spend within

the home environment, which may be related to

the decrease in reading scores.

CONCLUSION

It is important to consider a larger view of educa-

tional outcomes and refocus on the role of non-

cognitive skills in education. The classroom is

one place that promotes the development of non-

cognitive skills. As with other skills, students ben-

efit from being able to practice and develop their

noncognitive skills, which are important for later

learning and employment outcomes. Our study

explicitly identifies EAs as a site for students to

practice and develop their noncognitive skills. A

large portion of elementary-age students spend

time in EAs, and it is important to examine the

connection between how students spend their lei-

sure time and their classroom behaviors.

The findings of our study provide modest sup-

port for our expectation that participation in EAs

and its relationship with noncognitive skills medi-

ate the SES-achievement relationship. Although

EAs and noncognitive skills help explain part of

the association between SES and academic skills,

SES still has a direct relationship with academic

skills. Our results suggest that students who partic-

ipate in sports benefit more than students who par-

ticipate in other activities. However, better

measures would help us understand this relation-

ship further. The information we have about partic-

ipation in sports is limited to whether a child had

participated in the past year. It would be useful to

have more information about which sports students

participated in, the extent of participation, and the

quality of the program. It is important to recognize

that participation does not mean equal benefits or

equal quality in programming. By having these bet-

ter measures, we may be able to better examine the

mechanism through which the benefits of sports

work and to inform the study of inequalities. The

access and benefit differentials of participation in

EAs are another area of inequalities that are related

to schooling outcomes.21

Overall, the findings of this study provide lim-

ited support for our conceptual model. We did not

find strong mediating mechanisms between SES

and test scores, yet we did expand the focus of

EAs to include EAs as an additional source of

noncognitive skills. We expect that our findings

may not apply to adolescents in high school,

because they have greater agency, parental influ-

ence, and peer influence in decisions such as

extracurricular participation. Despite high rates

of participation among low-SES families, relative

differences by SES levels further perpetuate edu-

cational inequality. Our findings indicate that

EAs in childhood provide academic benefits for

students by providing them with a site to practice

and develop their noncognitive skills. Yet low-

SES students are still less likely to participate in

all types of EAs, providing students with disparate

access and opportunities to develop their noncog-

nitive skills. High-SES students have access to

such sites in a variety of settings, continuing to

provide these students with an advantage.

Leveling the playing field requires many interven-

tions in numerous different areas, but communi-

ties can begin by looking for opportunities after

the school bell rings and offering affordable,

high-quality extracurricular programs for students

regardless of their socioeconomic backgrounds.

ACKNOWLEDGEMENTS

We would like to thank Maureen Hallinan, Sean

Kelly, David Hachen, Mike Welch, Brian Miller,

Stephen Armet, and the anonymous reviewers for

their feedback and suggestions. An earlier version

of this article was presented at the 2007 annual con-

ference of the American Educational Research

Association.


NOTES

1. Leisure activities are those that students participate

in during their free time. Leisure activities can

include (but are not limited to) participating in

sports, talking to friends, reading, and watching

television (Larson and Verma 1999).

2. Bradley and Corwyn (2002) found that in general,

children from lower-SES families do not have

access to the same quantity and quality of recrea-

tional and learning materials. Moreover, Cheadle

(2008) and Bodovski and Farkas (2008) used

nationally representative data to examine the con-

cept of concerted cultivation, which includes EAs.

Both studies found that SES and concerted cultiva-

tion are positively related.

3. Parents following the concerted cultivation

approach encouraged and orchestrated activities,

both inside and outside the home, that sculpted their

children’s tastes and developed their skills. In con-

trast, parents who followed the natural growth

approach focused on caring for their children and

simply assumed that their children would develop

without active intervention and the shaping of their

leisure time (Lareau 2003).

4. Middle-class black families tend to live in neighbor-

hoods with higher crime and poverty rates com-

pared with white families both middle class and

poor (Pattillo-McCoy 2005). In addition, black mid-

dle-class families still have close connections to

poor family and friends, making their middle-class

position more precarious than that of white

middle-class families (Gosa and Alexander 2007).

5. As Figure 1 indicates, we acknowledge that SES

and race/ethnicity have effects on noncognitive

skills and achievement that are independent of the

effects of extracurricular participation. Figure 1

highlights the main focus of our study: whether

extracurricular participation contributes to SES

and racial-ethnic differences in achievement out-

comes (via noncognitive skills).

6. In contrast, Harris (1998) argued that children are

highly sensitive to features of the social context, and

she would be skeptical of the argument that skills

from one context carry over to other contexts. We

find Harris’s work both interesting and provocative,

but her view remains in the minority among both so-

ciologists and researchers in child development.

7. In addition to fostering the development of noncog-

nitive skills, EAs may prevent involvement in devi-

ant behaviors (Fletcher et al. 2003). Yet, research

on adolescent extracurricular participation shows

mixed results on whether deviant behavior is

decreased or increased by participation (Feldman

and Matjasko 2005). Some research indicates that

adolescent participation in activities such as sports

is related to increased deviant behaviors such as

underage drinking (Eccles et al. 2003; Feldman

and Matjasko 2005), which may be explained by

the peer groups associated with the activities

(Feldman and Matjasko 2005).

8. Mahoney et al. (2006) examined whether children

are overscheduled with organized activities during

their leisure time, as one may assume from

Lareau’s (2003) study. The overscheduling hypoth-

esis suggests that young children are constantly

involved in organized activities taking up extensive

amounts of their time (Mahoney et al. 2006).

Mahoney et al. found that extreme levels of extra-

curricular participation are not detrimental to chil-

dren on most measures of well-being. However,

Marsh and Kleitman (2002) found diminishing re-

turns to extreme levels of participation.

9. In her study, Broh offered three possible mechanisms:

(1) EAs build strong work habits and ‘‘character’’ in

students (the ‘‘developmental model’’); (2) students

in EAs join the ‘‘leading crowd,’’ which gives them

greater access to academically oriented peers (the

‘‘leading crowd’’ hypothesis); and (3) EAs generate

greater connections with adults outside of one’s fam-

ily (‘‘social capital’’), which serve as a resource for

higher achievement. Eccles et al. listed similar mech-

anisms, including (1) identity formation, (2) peer

groups, and (3) greater connections with adults out-

side of one’s family. Eccles et al. examined the asso-

ciation between activity participation and each of

these mechanisms rather than examining how much

of the ‘‘effects’’ of activity participation are explained

by these three categories.

10. The analytic sample includes those students who were

in both the first-grade and third-grade waves, hadparent

interviews, and had no missing data on the dependent

variables. The sample size for the analyses is 10,140.

11. The actual items that constitute the approaches to

learning scale are not available because of copy-

righting. On the basis of the ECLS-K user’s manual

(National Center for Education Statistics 2004), the

six original items are measured from 1 (never) to 4

(very often) and are combined to form the scale that

is used in the analyses.

12. We ran analyses including a measure of a ‘‘count’’

of EAs as an independent variable in addition to the

specific types of activities in which the students par-

ticipated. We considered a continuous count vari-

able and a categorical measure (zero, one or two,

three or four, and five or six activities). The count

variable is a proxy for the amount of time a student

is spending in EAs, which is a concern of the over-

scheduling hypothesis (see note 8). The inclusion of

the count variable is problematic when attempting

to account for ‘‘effects’’ of different types of activ-

ities and the number of activities in one model.

Including only a count variable as the main inde-

pendent variable in the models results in the loss

of information about the varying effects of certain

EAs. We decided to use the six binary variables


measuring participation in particular activities to

retain the most information in the analysis.

13. We do not have strong hypotheses about which EAs

will have what effects on noncognitive and aca-

demic skills. We do not have adequate information

about the EAs to make such hypotheses.

14. There were three eigenvalues greater than 1 (3.097,

1.305, and 1.124). There is a discontinuity between

these eigenvalues, and the difference between the first

two eigenvalues is greater than the second, suggesting

that a one-factor solution will work (Nunnally and

Bernstein 1994). This factor was used to create the

home activities scale, which includes the variables

helping the child do art (.625), playing games

(.661), teaching the child about nature (.639), build-

ing ‘‘things’’ (.677), and playing sports together

(.569), with the factor loadings in parentheses.

15. The variables used in the MI command include

race, first grade measure of approaches to learning,

family structure, composite SES, percentage of stu-

dents on free lunch within the school, gender, pri-

vate school, participation in dance, participation in

sports, participation in clubs, participation in music,

participation in art, participation in performing arts,

minutes per week a parent reads to child, home

activities scale, first grade reading test IRT, first

grade math test IRT, and first grade general knowl-

edge test IRT. Also, with MI, it is possible to get

values outside of the range for a variable. For exam-

ple, the variable t4learn ranges from 1 to 4. The

imputed values that were out of range on this vari-

able were restricted to from 1 to 4. In other words,

the out-of-range values were truncated to maintain

the scale of the variable. Other variables that were

kept in range include IRT scores, percentage free

lunch, and time spent reading to the child.

16. In the analyses in Tables 3 and 4, for which the

dependent variables are EAs, these models do

include imputed values for the dependent varia-

bles. The number of missing cases is very small

(roughly 64 cases, less than 1 percent of our

analytic size).

17. The dependent variable, approaches to learning, is

negatively skewed, with a concentration of students

receiving the highest, or close to the highest, value

on the scale. We can see this from the distribution

of the standardized residuals and the Shapiro-Wilk

test for normality (p \ .000). It is possible that

the data are right censored. The most frequent

response that a teacher could give for each item is

‘‘very often.’’ Could students consistently or always

display certain behaviors? To adjust for the censor-

ing of data, we also conducted tobit analyses. The

results of each model are consistent with the ordi-

nary least squares results in terms of significance

and direction. The coefficients of tobit models are

not easily interpretable because they are the result

of two predication equations (Roncek 1992). The

ordinary least squares regression coefficients are

presented in Table 5 for ease of interpretation.

18. We ran subsequent models with all of the racial and EA

participation interactions. Only a few interactions were

significant for the models in Tables 5, 6a, and 6b. In the

models presented in this article, we include a model

with only those significant racial interactions.

Additional models are available on request.

19. All predicted values for ordinary least squares mod-

els were conducted on one of the multiply imputed

data sets.

20. As can be seen from Table 2, there is near universal

levels of participation in EAs at high levels of SES.

An anonymous reviewer raised the question of

whether there are differences between those high-

SES students who participate and those who do

not. On closer examination, we find that there are

not large differences between high-SES students

who participate in at least one EA compared with

those high-SES students who do not participate in

EAs. It does not appear that those high-SES stu-

dents who do not participate in EAs are outcasts

in other measures compared with those high-SES

students who do participate.

21. Before anything can be said about what can be done

to use EAs as a way to reduce inequality, we need to

collect more information than whether a child par-

ticipates in an EA or not. Does frequency of partic-

ipation matter? Sports require more frequent

participation than clubs (Feldman and Matjasko

2005). In ECLS-K, we do not know how frequently

the children participate, whether the children are

currently involved, or how long the children have

been involved. All we know is that in the past

year, the children have participated, which is a lim-

itation of this study. Once more research into the

nuances of extracurricular participation is con-

ducted, we will know how to better set up EAs to

maximize their benefits. Another limitation of the

measure of extracurricular participation is that we

do not know the children’s desire to participate in

the activities. Do the children have a say in what

activities they participates in? Does it matter if

a child voluntarily participates in an activity as to

the relationship with schooling outcomes? This is

another example of why there needs to be more

detailed measures of extracurricular participation

when examining the relationship between participa-

tion and outcomes.

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BIOS

Elizabeth Covay is a doctoral candidate in the

Department of Sociology at the University of Notre

Dame. Her research focuses on education and stratifica-

tion. She is currently working on her dissertation, ‘‘The

Emergence and Persistence of The Black-White

Achievement Gap.’’

William Carbonaro is an associate professor in the

sociology department at the University of Notre Dame.

His primary research interests are in the areas of educa-

tion and social stratification. He is currently analyzing

how students’ strong and weak friendship ties affect

their educational outcomes.


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