This article was downloaded by: [University of Kent]On: 16 November 2014, At: 07:12Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

The Journal of Educational ResearchPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/vjer20

A Longitudinal Test of a Model of Academic Success forAt–Risk High School StudentsEileen S. Anderson a & Timothy Z. Keith ba Virginia Polytechnic Institute and State Universityb Alfred UniversityPublished online: 14 Nov 2012.

To cite this article: Eileen S. Anderson & Timothy Z. Keith (1997) A Longitudinal Test of a Model of Academic Success forAt–Risk High School Students, The Journal of Educational Research, 90:5, 259-268, DOI: 10.1080/00220671.1997.10544582

To link to this article: http://dx.doi.org/10.1080/00220671.1997.10544582

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in thepublications on our platform. However, Taylor & Francis, our agents, and our licensors make no representationsor warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Anyopinions and views expressed in this publication are the opinions and views of the authors, and are not theviews of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should beindependently verified with primary sources of information. Taylor and Francis shall not be liable for any losses,actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoevercaused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

A Longitudinal Test of a Model of Academic Success for At-Risk High School Students EILEEN S. ANDERSON Virginia Polytechnic Institute and State University

ABSTRACT Whereas previous researchers have described at-risk students' deficiencies, in the present study their aca­demic success was investigated. Longitudinal data from 8,100 high school students participating in a national study were used to test a model of at-risk students' school learning. The results indicated that ability, quality of schooling, student motivation, and academic coursework are important predic­tors of academic achievement. Although the present findings indicate that student motivation may have a stronger impact on at-risk students' achievement than on the achievement of high school students in general, overall school learning influ­ences appear very similar to those found for high school stu­dents in general.

D espite recent gains in achievement test scores, African American, Hispanic, and low-income students contin­

ue to achieve well below Caucasian, Asian, and high­income students (Koretz & Houts, 1986; Ogle & Alsaiam, 1990). In addition, African American and Hispanic high school students historically drop out of school at higher rates than their Caucasian peers do and are less likely to return to complete graduation requirements within 4 years (Ogle & Alsalam, 1990; Snyder & Hoffman, 1990). Unfortunately, our understanding of the factors that explain the achieve­ment patterns of at-risk students is limited. The literature on at-risk learners largely has described what at-risk students lack--personally, or by way of their families, communities, or schools--that might lead to academic failure (Meier & Stewart, 1991 ; Pallas, Natriello, & McDill, 1989). To opti­mize learning environments and maximize the potential of at-risk learners, educators must also understand which fac­tors contribute to academic success (Bereiter, 1985; Scarr, 1988; Wang, 1990) and how these factors exert their positive influence (Cool & Keith, 1991; Linney & Seidman, 1989). Our purpose in the present study was to develop and test a theoretical model of academic success for at-risk high school students. We proposed a model based on previous theories and research in school learning at1d used responses from over 8,000 non-Asian minority and low-socioeconom­ic-status (SES) high school students in a very large national

259

TIMOTHY Z. KEITH Alfred University

longitudinal educational survey to estimate the variables in the model and test the fit of the model to the data.

Modeling Academic Achievement

Models of school learning focus on hypothesizing and testing the simultaneous, direct and indirect, causal relations of environmental, personal, and background variables to academic achievement. Researchers can often use causal analysis or structural modeling analysis techniques to test such models with nonexperimental data. The advantages of causal analysis include reliance on theory--models are drawn a priori with important variables and causal relations specified--and the examination of direct, indirect, and total effects of variables in the model (in contrast to simple pre­diction techniques that focus only on direct effects; Keith, 1993). Carroll (1963, 1989), in what is often credited as being the first model of school learning, stressed the impor­tance of influences exerted by interactions among (a) time­needed versus time-allowed to learn, (b) the student's moti­vation and ability to learn, and (c) the instruction received by the student. Wiley and Harnischfeger (1974) expanded this model by adding teacher and pupil background variables to the interactive triad. These models, along with those of Ben­nett (1978), Bloom (1980), and Cooley and Leinhardt (1975), have been classified as being essentially "time-mod­els," in contrast to "learning theory models" that deal more specifically with the conditions necessary for classroom learning (Haertel, Walberg, & Weinstein, 1983, p. 77).

The Model of Educational Productivity (Walberg, 1981, 1986), a time-model of school learning, was a precursor to the model proposed in the present research. Walberg's model includes four essential factors (Student Ability, Stu­dent Motivation, and Quality and Quantity of Instruction) believed to counterbalance each other such that a deficien­cy in one area could be compensated for by a surplus

Address correspondence to Eileen S. Anderson, Center for Research in Health Behavior; Department of Psychology, Virginia Polytechnic Institute & State University, Blacksburg, VA 24061-0436.

Dow

nloa

ded

by [

Uni

vers

ity o

f K

ent]

at 0

7:12

16

Nov

embe

r 20

14

260

(albeit, disproportionately large) in another. Four supple­mentary factors (Home Environment, School Environment, Peer Influence, and Mass Media) were included and con­sidered important because they address "large blocks of time devoted to potentially educational . . . activities" (Haertel et al., 1983, p. 76).

The Model of School Learning for At-Risk High School Students

The school learning model for at-risk high school stu­dents proposed here included Walberg's four essential fac­tors and a Home Environment factor believed to contribute to ethnic differences in achievement (Steinberg, Dornbusch, & Brown, 1992) and to enhance educational equity (Jensen, 1985; Mullin & Summers, 1983; Walberg, 1990; see Figure 1). This model expanded on previous school learning mod­els by including more broadly defined quality of instruc­tion, student motivation, and home environment variables in predicting at-risk student achievement. As a simple recur­sive model, we hypothesized unidirectional causal relations among the variables, as indicated by arrows in Figure I. The variables in the model were operationally defined by com­posites constructed from student responses to items from a national longitudinal survey of high school students (see Method below). A detailed description of the item con­struction of these variables can be found in the Appendix.

Student ability classically has been found to exert the strongest single influence on academic achievement. This effect holds across many operational definitions of ability, including standardized vocabulary test scores (Cool & Keith, 1991; Keith & Page, 1985), prior standardized achievement test scores (DiPrete & Gallaher, 1982), prior school grades (Walberg, Fraser, & Welch, 1986), and nonverbal tests of ability (Rehberg & Rosenthal, 1978). Keith (1993) found that this powerful relation between ability and achievement was maintained for White and minority students. In models of school learning, ability also has been shown to influence a host of other variables that in turn influence high school learning: (a) number of academic courses taken (Cool & Keith, 1991; Keith & Page, 1985), (b) quality of instruction in the school (Keith & Cool, 1992), (c) time spent on home­work (Fehrmann, Keith, & Reimers, 1987), (d) parental involvement (Rehberg & Rosenthal, 1978), (e) time spent watching TV (Fehrmann et al., 1987), (f) motivation (DiPrete & Gallaher, 1982; Parkerson, Lomax, Schiller, & Walberg, 1984), and (g) encouragement received from others to go to college (DiPrete & Gallaher, 1982.). In the present study, we estimated student ability from standardized vocabulary test scores from the 1980 High School and Beyond (HSB) survey.

Quality of instruction previously has been defined as one of the following: (a) the expectations and interest level of teachers (Good & Weinstein, 1986; Linney & Seidman, 1989; Wang, 1990), (b) resource allocation and condition of school facilities (Griswold, Cotton, & Hansen, 1986), (c) establishment and consistent enforcement of rules (Gris-

The Journal of Educational Research

wold et al., 1986; Purkey & Smith, 1983; Rutter, 1983), and (d) a general emphasis on learning and teaching (Purkey & Smith, 1983; Wang, 1990). Models of school learning investigating quality of instruction with unidimensional variables have yielded mixed results (e.g., Parkerson et al., 1984; Uguroglu & Walberg, 1986; Walberg et al., 1986). Cool & Keith (1991) considered a composite variable of quality of instruction defined by student ratings of teacher interest, quality of instruction, and reputation of the school within the community. This multidimensional quality of instruction variable directly influenced students' motiva­tion, enrollment in academic coursework, and time doing homework, and indirectly through these variables, quality of instruction influenced academic achievement (Cool & Keith, 1991 ). These findings were later supported when Keith (1993) examined the influence of quality of instruc­tion across ethnic groups. In the present study, we expand­ed this operational definition of quality of instruction to include composite ratings of schoolwide discipline and teacher characteristics and labeled it quality of schooling.

Home environment, or parental involvement, has been found to contribute to ethnic differences in achievement (Steinberg et al., 1992), has been recommended to enhance educational equity (Walberg, 1990), and has been cited as an important component in some compensatory education programs (Jensen, 1985; Mullin & Summers, 1983). Sever­al investigations of the effects of parental involvement on adolescent achievement within a model of school learning, however, suggest that the influence of parental involvement may diminish as students becoine older (Keith, 1991) and may not hold for high school students (especially when standardized measures of academic achievement are used; cf. DiPrete & Gallaher, 1982, Fehrmann et al., 1987; Keith, Reimers, Fehrmann, Pottebaum, & Aubey, 1986). In the research presented here, we estimated parental involvement from composite ratings of school-related parental planning, parental monitoring, parental aspirations, and parental con­tact with school personnel.

Student motivation historically has been viewed as "per­haps the most significant area of difference between lower-[class] and middle-class school populations ... " (Goldberg, 1967, p. 43). Similarly, Sewell and Price (1989) argued that "a greater emphasis on motivation should be salu­tary in understanding and enhancing minority achievement" (p. 16). Motivation, a multidimensional construct that includes cognitive, environmental, and behavioral compo­nents (Weiner, 1990), often has been defined solely by its cognitive component, with fairly global estimates (see Haer­tel et al., 1983). Carroll ( 1963, 1989) has been the noted exception; rather than focusing on beliefs, expectancies, self­efficacy, and other "attitudinal" expressions of motivation, Carroll (1989) described the actual amount of time spent on learning (or perseverance) as the "operational definition of motivation for learning" (p. 26). Estimates of motivation that have included behavioral expressions (e.g., time spent on learning) have been more strongly related to achievement

Dow

nloa

ded

by [

Uni

vers

ity o

f K

ent]

at 0

7:12

16

Nov

embe

r 20

14

Figure 1. Model of School Learning for At-Risk High School Students

0 .-4

.24

School Quality

r.92

Parental ~~ fr;olvemen~

=---Parental Involvement

Academic Coursework

~~

~/Academic Achievement

Chi-Square = 32.34 RMSEA = 0.036

AGFI • 0.99 RMR= 0.01

TLI • 0.99

~ ~

i .... ~ ~

J ~

~ ....

Dow

nloa

ded

by [

Uni

vers

ity o

f K

ent]

at 0

7:12

16

Nov

embe

r 20

14

262

than those that have relied on cognitive, affective, or attitudi­nal expressions alone. For example, when motivation was defined by measures of school attendance and truancy (behavioral expressions) combined with measures of interest and school enjoyment (cognitive expressions), moti­vation exerted a strong influence on achievement in a model predicting school grades (DiPrete & Gallaher, 1982). Simi­larly, in studies where researchers used only measures of behavioral expressions of motivation (e.g., "completion of academic tasks" or homework), motivation has been found to be predictive of achievement test scores (Grabe, 1982; Wal­berg et al., 1986). In contrast, studies defining motivation by measures of cognitive expressions without behavioral expres­sions have found much weaker effects for motivation on aca­demic achievement (Keith & Cool, 1992; Parkerson et al., 1984; Uguroglu & Walberg, 1986).

The components of academic motivation believed impor­tant for at-risk students include (a) effort and choice of activities (e.g., homework; Sewell & Price, 1989), (b) aca­demic self-efficacy or belief in one's ability to succeed at a given task (Broderick & Sewell, 1985), and (c) locus of control (Graham, 1988). Linking behavioral and cognitive definitions of motivation was also cited as essential to understanding academic achievement among minority stu­dents (Sewell & Price, 1989), which may explain Keith's (1993) findings of no evidence of direct effects and only small indirect effects of cognitive expressions of motivation alone on achievement for non-Asian minority students. In the present study, we included measures of both cognitive expressions (i.e. , student ratings of locus of control, interest in school, and educational aspirations) and behavioral expressions of motivation (i .e., student reports of conduct at school, attendance, preparation for class, and weekly hours of homework completed).

Quantity of instruction, or number of academic courses completed, has been found uniformly to exert a powerful influence on achievement test scores for both White and minority students (Alexander & Pallas, 1984; Keith & Page, 1985; Walberg et al. , 1986; Wolfle & Ethington, 1986). Conversely, the effect of enrollment in general or vocation­al curricula (which have fewer academic courses than col­lege-track curricula have) on standardized reading and mathematics scores has been negative (Myers, Milne, Baker, & Ginsburg, 1987; Oakes & Lipton, 1990). In the present study, we estimated students' quantity of instruction from student reports of the number of academic courses they completed.

The causal ordering specified by the model was based on previous work with models of school learning buttressed by the longitudinal nature of the data (see Figure 1). Estimates of exogenous background variables (i.e., family SES, gen­der, and ethnicity) and ability came from data collected when students in the study were sophomores. Estimates of quality of schooling and the remainder of the variables in the model came from data gathered 2 years later. Quality of schooling has been shown to depend in part on family SES

The Journal of Educational Research

and ability (Rutter, 1983) and to influence student motiva­tion and number of academic courses taken (Cool & Keith, 1991 ; Uguroglu & Walberg, 1986). We reasoned that, espe­cially at the high school level, parental involvement would also depend to some extent on quality of schooling; parental involvement, motivation, and academic coursework there­fore follow quality of schooling in the model. Family SES and the other background variables in the model precede quality of schooling. Motivation has been shown to be influenced by parental involvement (DiPrete & Gallaher, 1982), ability, and quality of schooling (Cool & Keith, 1991; Uguroglu & Walberg, 1986) and to subsequently in­crease the number of academic courses high school students take (Cool & Keith, 1991; Rehberg & Rosenthal, 1978). All variables in the model may influence academic achieve­ment, either directly (as indicated by the paths) or indirect­ly (through other variables in the model).

Method

Participants

Students termed at risk often share demographic charac­teristics with groups that have achieved poorly in the past (i.e. , non-Asian minority and low-SES students--Bereiter, 1985). Ralph (1989) found that such students are more like­ly to finish school without basic academic skills and are therefore at risk of becoming educationally disadvantaged as adults. We selected participants in the present research from the sample of high school students from the sopho­more cohort of the High School and Beyond Longitudinal Study (National Center for Education Statistics [NCES], 1983) who indicated they were of non-Asian minority ori­gin or whose SES composite scores fell within the bottom quartile of the SES range of the total sample. Although authors of past studies using the HSB data set have looked at correlates of school learning for different ethnic groups (cf. Keith, 1993) and for different levels of family SES (cf. King, 1983), in the present study we used a more complete sample of at-risk students. Selected students participated in both the Base Year (1980) and First Follow-Up (1982) In­School Surveys; they were lOth graders in 1980 and, for the most part, 12th graders in 1982. The final sample included 8, I 00 at-risk students.

This sampling procedure eliminated from the study stu­dents who dropped out of school before their sophomore year or who dropped out and did not return to school between the 1980 and 1982 surveys. Because the dropout rate may be differentially distributed across SES and ethnic groups (Stern, 1987), this selection procedure could bias the findings by eliminating some of the lowest achieving at-risk learners. However, supplemental analysis of the distribution of achievement scores for at-risk students who dropped out reveals it to be similar to the distribution for those at-risk stu­dents who stayed in school. The vast majority of low-achiev­ing students do not drop out of high school, and "dropping

Dow

nloa

ded

by [

Uni

vers

ity o

f K

ent]

at 0

7:12

16

Nov

embe

r 20

14

May/June 1997 [Vol. 90(No. 5)]

out of school does not necessarily coincide with being edu­cationally disadvantaged [in adulthood]" (Ralph, 1989, p. 400). Additional infonnation concerning these supplemental analyses is available from the first author by request.

Instrumentation

The HSB study began as a nationally representative, two-stage probability sample of 1 ,016 high schools, with schools selected based on their estimated enrollments. At present, HSB includes a base-year survey and three fol­low-ups. During the Base-Year Survey, up to 36 sophomores and 36 seniors were selected randomly from each school. During each survey year, students answered questions about family background, school experiences, perceptions of themselves and peers, and postsecondary educational aspi­rations and plans. Respondents also completed a battery of ability and achievement tests (NCES, 1983). The psycho­metric qualities of these surveys and tests appear to be ade­quate for research purposes (see Heyns & Hilton, 1982).

Procedure

We measured the variables in the model by items and multi-item composites from the Base-Year (1980) and First Follow-Up (1982) Sophomore Cohort In-School Sur­veys of the HSB. We measured ethnicity and gender vari­ables by single items, and we measured family SES and ability variables by composites provided as part of the HSB data. We formed parental involvement, quality of schooling, motivation, and academic coursework variables from items on the HSB First Follow-up Survey (see Appendix). We selected these items based on previous work by Keith and colleagues (e.g., Keith, 1993), previous research (especially Walberg, 1986), common definitions, and theory. We subjected students' responses to these selected items to factor analysis and used the results to guide our final selection of items.

Specifically, we used SPSS-X (3rd edition) data analysis software to conduct factor analysis with principal axis fac­toring extraction, varimax rotation, and pairwise deletion of the responses of the full HSB sample to subsets of items selected because they were related to the constructs in the model. At this step, factor loadings detennined how items would be clustered to fonn subcomposites of variables in the model. We eliminated items with loadings of less than .35 from the item pool. There were two important excep­tions to this procedure; first, although the item asking stu­dents how far they believed their parents expected them to go in school loaded heavily (.72) on a factor with items ask­ing about the student's own educational aspirations (see Appendix), we believed parental aspirations to be an impor­tant component of parental involvement and included it as a subcomposite of that variable. Second, although the item asking students how much time they spent on homework each week also met our criteria for inclusion in the educa-

263

tiona! aspirations subcomposite, we included it as a single­item motivation subcomposite (i.e., preparation) in accor­dance with previous theory (cf. Carroll, 1989).

We fanned subcomposites by converting item raw scores to z-scores and then averaging the z-scores (Keith & Cool, 1992). We made final modifications to the subcomposites based on analyses of their internal consistency (Cronbach 's alpha) by eliminating items to increase reliability (i.e., one item from the motivation subcomposite interest and the parental involvement subcomposite parental monitoring, and one item from each of the quality of schooling sub­composites were eliminated. Finally, following Guilford (1954), we computed reliabilities for variable composites.

Analysis

We analyzed the model with data from at-risk high school students completing both 1980 and 1982 In-School Surveys. We used structural equations analysis (LISREL-8) to fit our model to the HSB data (Joreskog & Sorbom, 1993). In full LISREL models, components of each com­posite variable (e.g., the subcomposites listed in the Appen­dix) are used as measured indicators of variables within the model (called latent constructs). In deference to the prelim­inary nature of the proposed model, we used our composite variables as single indicators of variables (enclosed ellipti­cally in Figure 1). Estimates of the quality of our measured single indicators (i.e., reliability coefficients for the com­posites) are built into the analysis.

Figure I displays the direct effects and fit statistics gener­ated by the LISREL analysis of the data. The paths leading back from the variables in the ovals point to the related com­posite variables fanned from the HSB data; the path coeffi­cients reflect error correction based on reliability estimates (i .e., 1-r). The paths between the variables in the ovals indi­cate the hypothesized causal relations between variables; the path coefficients are the direct effects generated by LISREL. These coefficients (similar to beta weights from multiple regression) can be interpreted by using a general rule of thumb that manipulable effects are meaningful, but small when path coefficients are > .05 and < .1 0, moderate when they are > .11 and < .25, powerful when > .25 (Keith, 1993; Pedhazur, 1982). LISREL also generates coefficients for indirect and total effects of variables (illustrated in Table 2). A comparison of the direct, indirect, and total effects of the quality of schooling variable on academic achievement demonstrated the importance of these coefficients. Quality of schooling did not appear to directly influence achieve­ment; however, the effect of quality of schooling on student motivation and academic coursework (see Figure 1) make its total contribution to achievement important (.12).

Results

Table 1 contains the means, standard deviations, reliabil­ities, and intervariable Pearson correlations used in the

Dow

nloa

ded

by [

Uni

vers

ity o

f K

ent]

at 0

7:12

16

Nov

embe

r 20

14

264

analyses. Figure 1 contains the overall results of the struc­tural equations analysis. Table 2 contains direct, indirect, and total effects of each variable on achievement.

Because the model was overidentified--there were no paths from gender to ability, quality of schooling, or achievement--the resulting positive degrees of freedom allowed a covariance matrix predicted by the model to be compared with the actual (sample) matrix used to estimate the model, thus providing evidence of the fit of the model to the data. The fit statistics listed at the bottom of Figure I suggest a good fit of the proposed model to the HSB data and can be interpreted as follows: First, the chi-square sta­tistic is significant, as could be expected with very large samples (e.g., our minimum N of 7,355) when tiny differ­ences between predicted and actual matrices can lead to a significant chi-square (Bentler & Bonnett, 1980; Marsh, Balla, & McDonald, 1988). Second, the root mean square error of approximation (RMSEA) indicates a close fit when it falls below .05 (Browne & Cudeck, 1993). Third, the adjusted goodness-of-fit index (AGFl) approaches 1.0 as the fit improves. Fourth, the root mean square residual (RMSR) represents the average deviation of the predicted from the actual matrix. (The standardized RMSR---based on analysis of a correlation matrix--is shown.) Last, the Tucker Lewis Index (TLI) approaches 1.0 as the fit improves and provides a comparison of the proposed model to a null model (one in which it is assumed that the variables are not correlated).

Effects on Achievement

Using the rule of thumb described earlier, each of the variables in the final model, except parental involvement, exerted a meaningful total effect (E

1 > .05) on academic

achievement among the sample of at-risk high school stu­dents. Family SES, ethnicity, gender, ability, quality of schooling, motivation, and completion of academic course­work were supported by this large, longitudinal data set as

The Journal of Educational Research

significant and important predictors of achievement for at­risk high school students (i.e., p£

1 < .01).

Ability exerted the most powerful influence of any of the variables in the school learning model. Student ability affected achievement directly and also indirectly through quality of schooling, student motivation, and completion of academic coursework. Academic coursework also exerted a powerful effect on academic achievement. It appears that each additional academic course that an at-risk student completes can be expected to result in an increase of one eighth of a standard deviation in academic achievement test scores. Student motivation exerted moderate direct effects on achievement and moderate indirect effects mediated by academic coursework.

Quality of schooling and parental involvement exerted their positive influences on achievement indirectly. Both quality of schooling and parental involvement strongly influenced student motivation, which subsequently exerted a powerful total effect. Unlike quality of schooling, which had no meaningful direct effect on achievement among at­risk high school seniors, parental involvement had a small negative direct effect . such that the direct effect virtually canceled out its positive indirect influence.

The exogenous variables in the model were also impor­tant predictors of achievement for the at-risk sample. The indirect effects of family SES and ethnicity were mediated largely by ability. Ethnicity also directly influenced achievement. Gender's effect on achievement was strongly mediated by student motivation (i.e., girls having higher motivation than boys) and to some extent by coursework (i.e., boys somewhat more likely than girls to complete aca­demic coursework).

Causal Relations Among Malleable Variables

These findings suggest that each of the theory-derived variables in the proposed model (except parental involve-

Table I.-Pearson Correlations, Means, and Standard Deviations of Variables for At-Risk Sample

Variable 2 3 4 5 6 7 8 9

I. Ability .810 2. School quality .172 .835 3. Parental .011 .153 .792 4. Motivation .161 .323 .319 .808 5. Coursework .379 .215 .234 .382 .791 6. Achievement .672 .240 .041 .311 .509 .820 7. Family SES .208 .097 .266 .121 .242 .184 8. Ethnicity .170 .001 -.257 - .083 -. 105 .186 - .330 9. Gender -.081 - .022 .004 .237 - .018 - .010 -.089 .043

M 45.623 - .075 - .020 -.034 .007 46.886 -.521 .267 .523 SD 9.221 .519 .584 .468 .599 7.616 .668 .423 .500

Note. Minimum pairwise N = 7 ,355. Reliabilities for constructed variables are reported on the diagonal. We estimated error terms for gender (.01), ethnicity (.10), and family SES (.15) based in part on prior work (i.e., Keith, 1993). The sample's non-standardized mean number of academic courses completed was 3.2 (SD = 2.42).

Dow

nloa

ded

by [

Uni

vers

ity o

f K

ent]

at 0

7:12

16

Nov

embe

r 20

14

May/June 1997 [Vol. 90(No. 5)]

Table 2.-Direct, Indirect, and Total Effects of Variables in the Initial Model on Achievement

Effect

Variable Direct Indirect Total

Family SES .03 .33** .36** Ethnicity .13** .22** .35** Gender .05** .05** Ability .62** .18** .80** Quality of schooling .04 .08** .12** Parental involvement - .08* .I I** .03 Motivation .14** .10** .24** Coursework .29** .29**

*p < .05. **p < .01.

ment) contributed to general academic achievement among at-risk high school students. This influence was partially direct and partially indirect. A closer examination of the indirect effects, or the relations among the endogenous vari­ables, suggested mechanisms for maximizing malleable variables influencing at-risk students' academic success. The malleable variables presumably can be increased or decreased through intervention, and thus through them, achievement can be modified (Bloom, 1980). Evidence has been found to support the notion that quality of schooling, parental involvement, motivation, and academic coursework can indeed be manipulated (for references, see Bennett, 1987). Three of the malleable variables in the present model (quality of schooling, motivation, and academic coursework) positively influenced at-risk students' achievement (see Table 2). Of these malleable variables, academic coursework exerted the strongest effect on achievement; thus, variables that influenced coursework are also worth considering (see Figure 1). Of the malleable variables leading to completion of more academic courses, motivation exerted a powerful influence; students who were highly motivated also took more academic courses. Quality of schooling and parental involvement also influenced completion of academic course­work through their positive impact on motivation.

Discussion

The research reported here suggests that ability, quality of schooling, student motivation, and enrollment in acade­mic coursework make important contributions to the acade­mic success of at-risk high school students. As would be predicted by previous research, individual ability and com­pletion of academic coursework exerted the most powerful direct effects on academic achievement (Cool & Keith, 1991 ; Keith & Page, 1985; Walberg, 1984; Wolfle & Ethington, 1986). In addition, the impact of quality of schooling on academic achievement, although indirect, was important (cf. Cool & Keith, 1991).

Student motivation had a strong total effect on achieve­ment among low-SES and non-Asian minority students in the

265

present study. In contrast to Keith's (1993) exploration of effects of motivation and homework on achievement among different ethnic groups that indicated no effects of cognitive motivation and inconsistent effects of homework, the results of the present study suggest that student motivation (includ­ing completion of homework) exerted a strong overall effect on academic achievement among at-risk learners.

In contrast, we found virtually no evidence of an overall effect of parental involvement on achievement among at­risk adolescents. Although failure to find an effect for parental involvement is consistent with previous studies (cf. Keith et al., 1986), the pattern of negative direct and posi­tive indirect effects found here has not been previously reported. We should note that the parental involvement vari­able used here was expanded from previous work (cf. Fehrmann et ill., 1987) to include student ratings of parental contact with the school personnel as well as parental aspi­rations, parental monitoring of school progress, and parental assistance with after-high-school planning. With the exception of parental aspirations, the types of parental involvement assessed may conflict with adolescent devel­opment (and concurrent need for autonomy, independence, and detachment from family members) and may not be appropriate at the high school level. This complex relation will need further investigation.

At-Risk Students Versus Students in General

Comparing at-risk high school students with high school students in general might suggest differential effects of some variables for the at-risk group. Broad comparisons be­tween Keith and Cool (1992) and the present study indicate that the associations within the models are very similar. For example, ability and academic coursework exerted powerful effects on achievement among at-risk students and high school students in general, and the influence of motivation and quality of schooling was important to each group (in large part because of their positive impact on enrollment in academic coursework). Differences in the size of effects for the two groups are difficult to interpret because of important differences in variable definitions (noted in the introduction and background sections above). Nevertheless, it is impor­tant to note that student motivation may have exerted a stronger effect on at-risk student achievement in both direct and total effects. We concede that this enhanced effect of motivation could have been due entirely to measurement differences, but predictions that at-risk students' achieve­ment might be more sensitive to changes in motivation (e.g., Sewell & Price, 1989) cannot be overlooked. Student moti­vation may have a stronger influence on at-risk youth than on youth in general; further research will be needed to answer this question.

Similarly, further study is needed to validate the school learning model and variables as defined here. Although the model and initial variable definitions were developed a priori to data analysis, variable definitions were limited to

Dow

nloa

ded

by [

Uni

vers

ity o

f K

ent]

at 0

7:12

16

Nov

embe

r 20

14

266

items available in the extant data set. Cross-validation of the model or tests against alternate models are also need­ed in the next steps of research in school learning of at­risk students.

Nevertheless, the results of this study support the importance of quality of schooling, student motivation, and academic coursework for the academic success of at­risk high school students. These results have several important implications for educating at-risk youth. First, these results go beyond describing demographic differ­ences in achievement and suggest ways to improve the learning of at-risk youth. For example, completion of aca­demic coursework regardless of student motivation or ability is a strong predictor of academic achievement. At­risk students who take two academic classes per year in high school might expect to increase their achievement test scores by almost a standard deviation. This is due in part to the low number of academic courses completed by the at-risk students sampled (i.e., about three courses over 4 years of high school), which suggests that historic and continuing trends in tracking at-risk students to nonacade­mic curriculum may indeed negatively affect their acade­mic achievement (cf. Oakes & Lipton, 1990). Further, educators might be able to enroll more at-risk students in academic classes by intervening in student motivation (perhaps through quality of schooling and specifically tar­geted parental involvement). Second, and more important, these results (when compared with those of other research) suggest that the important influences on the learning of at-risk youth are the same as those for students in general. This finding, in tum, suggests that school reforms that increase school quality, student motivation, and enrollment in academic coursework may improve the learning of at-risk as well as nonrisk youth.

NOTE

The research reported here is based in part on the first author's doctoral dissertation in School Psychology at Virginia Polytechnic Institute & State University. We are grateful to Lee M. Wolfle, Thomas H. Hohenshil, Thomas H. Ollendick, and Gloria W. Bird for their assistance and to Vir­ginia Tech for personnel and computer support. We are responsible for any errors in this article and for the opinions expressed.

REFERENCES

Alexander, K. L., & Pallas, A. M. (1984). Curriculum reform and school performance: An evaluation of the "New Basics." American Journal of Education, 92, 391-420.

Bennett, S. M. ( 1978). Recent research on teaching: A dream, a belief, and a model. British Journal of Educational Psychology, 48, 127-147.

Bennett, W. J. ( 1987). What works: Schools that work: Educating disad­vantaged children. Washington, DC: U.S. Department of Education.

Bentler, P. M. , & Bonnet, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606.

Bereiter, C. (1985). The changing face of educational disadvantagement. Phi Delta Kappan, 67, 538-541.

Bloom, B. S. (1980). The new direction in educational research: Alterable variables. Phi Delta Kappan, 61, 382-385.

The Journal of Educational Research

Broderick, P. , & Sewell, T. (1985). Attributions for success and failure in children of different social class. The Journal of Social Psychology, 125, 591-599.

Browne, M. W. , & Cudeck, R. (1993). Alternative ways of assessing model fit. InK. A. Bollen & J. S. Long (Eds.), Testing structural equation mod­els (pp. 136-162). Newbury Park, CA: Sage.

Carroll, J. B. (1963). A model for school learning. Teachers College Record, 64, 723-733.

Carroll, J. B. (1989). The Carroll Model: A 25-year retrospective and prospective view. Educational Researcher; 18( I ), 26-31.

Cool, V. A., & Keith, T. Z. (1991). Testing a model of school learning: Direct and indirect effects on academic achievement. Contemporary Educational Psychology, 16, 28-44.

Cooley, W., & Leinhardt, G. (1975). The application of a model for inves­tigating classroom processes. Pittsburgh, PA: The University of Pitts­burgh Learning Research & Development Center.

DiPrete, T. A., & Gallaher, H. E. (1982, August) . Socialization and status as distinguishable sources of educational success: An alternative to the accumulation perspective on the early socioeconomic career. Paper pre­sented at the annual meeting of the American Psychological Associa­tion, Washington, DC.

Fehrmann, P. G., Keith T. Z., & Reimers, T. M. (1987). Home influences on school learning: Direct and indirect effects of parental involvement on high school grades. The Journal of Educational Research, 80, 330-337.

Goldberg, M. L. (1967) Factors affecting educational attainment in depressed urban areas. In A. H. Passow, M. L. Goldberg, & A. J . Tan­nenbaum (Eds.), Education of the disadvantaged (pp. 31-61). New York: Holt.

Good, T. L., & Weinstein, R. ( 1986). Schools make a difference: Evidence, criticisms and new directions. American Psychologist, 41, 1090-1097.

Grabe, M. (1982). Effort strategies in a mastery instructional system. The quantification of effort and the impact of effort on achievement. Con­temporary Educational Psychology, 7, 327-333.

Graham, S. (1988). Can attribution theory tell us something about motiva­tion in Blacks? Educational Psychologist, 23, 3-21.

Griswold, P. A., Cotton, K. J., & Hansen, J. B. (1986). Effective compen­satory education source book: Volume 1: A review of effective educa­tional practices. Washington, DC: U.S. Government Printing Office.

Guilford, J. P. (1954). Psychometric methods (2nd ed.). New York: McGraw Hill .

Haertel, G. D., Walberg, H. J., & Weinstein, T. (1983). Psychological mod­els of educational performance: A theoretical synthesis of constructs. Review of Educational Research, 53, 75-91 .

Heyns, B., & Hilton, T. L. (1982). The cognitive tests for High School and Beyond: An assessment. Sociology of Education, 55, 89-102.

Jensen, A. R. (1985). Compensatory education and the theory of intelli­gence. Phi Delta Kappan, 67, 554-558.

Joreskog, K. G., & Sorbom, D. (1993). USREL 8. Chicago: Scientific Software. Keith, T. Z. (1991 ). Parent involvement and achievement in high school. In

S. Silvern (Ed.), Advances in reading/language research: Literacy through family, community and schaol interaction (Vol. 5, pp. l25-141 ). Greenwich, CT: JAI Press.

Keith, T. Z. (1993). Causal influences on school learning. In H. J. Walberg (Ed.), Analytic methods for educational productivity (Vol. 3, pp. 21-47). Greenwich, CT: JAI Press.

Keith, T. Z., & Cool, V. A. (1992). Testing models of school learning: Effects of quality of instruction, motivation, academic coursework, and homework on academic achievement. School Psychology Quarterly, 7, 207-226.

Keith, T. Z ., & Page, E. B. (1985). Do Catholic high schools improve minority student achievement? American Educational Research Jour­nal, 22, 337-349.

Keith, T. Z., Reimers, T. M., Fehrmann, P. G ., Pottebaum, S. M. , & Aubey, L. W. ( 1986). Parental involvement, homework and TV time: Direct and indirect effects on high school achievement. Journal of Educational Psychology, 78, 373-380.

King, J. L. (1983). Selected family, student, and school variables which relate to education attainment of high school seniors across classes. Unpublished doctoral dissertation, Virginia Polytechnic Institute & State University, Blacksburg, VA.

Koretz, K., & Houts, P. L. (1986). Trends in educational achievement. Washington, DC: The Congressional Budget Office.

Dow

nloa

ded

by [

Uni

vers

ity o

f K

ent]

at 0

7:12

16

Nov

embe

r 20

14

May/June 1997 [Vol. 90(No. 5)]

Linney, 1. A., & Seidman, E. (1989). The future of schooling. American Psychologist, 44, 336-340.

Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confrrrnatory factor analysis: The effect of sample size. Psy­chological Bulletin, 103, 391-410.

Meier, K. J., & Stewart, J. (1991). The politics of Hispanic education. New York: State University of New York Press.

Mullin, S. P., & Summers, A. A. (1983). Is more better? The effectiveness of spending on compensatory education. Phi Delta Kappan, 64, 339-374.

Myers, D. E., Milne, A. M., Baker, K., & Ginsburg, A. (1987). Student dis­cipline and high school performance. Sociology of Education, 60, 18-33.

National Center for Education Statistics (1983). High School and Beyond 1980 Sophomore Cohort First Follow-Up ( 1982): Data file user's man­ual. Washington, DC: U.S. Government Printing Office.

Oakes, J., & Lipton, M. (1990). Tracking and ability grouping: A structur­al barrier to access and achievement. In J. I. Goodlad & P. Keating (Eds.), Access to knowledge: An agenda for our nation's schools (pp. l87-204). New York: College Entrance Examination Board.

Ogle L. T., & Alsalarn, N. (1990). The condition of education, 1990. Vol­ume 1: Elementary and secondary education. Washington, DC: U.S. Department of Education.

Parkerson, J . A. , Lomax, R. G., Schiller, D. P., & Walberg, H. J. (1984). Exploring causal models of educational achievement. Journal of Educa­tional Psychology, 76, 638-646.

Pallas, A., Natriello, C., & McDill, E. ( 1989). The changing nature of the disadvantaged population: Current dimensions and future trends. Edu­cational Researcher; 1 8(5), 16-22.

Pedhazur, E. J. (1982). Multiple regression in behavioral research: Expla­nation and Prediction. New York: Holt, Rinehart & Winston.

Purkey, S. C., & Smith, M. S. (1983). Effective schools: A review. The Elementary School Journal, 45, 427-451 .

Ralph, J. (1989). Improving education for the disadvantaged: Do we know who to help? Phi Delta Kappan, 69, 395-401.

Rehberg, R. R., & Rosenthal, E. R. (1978). Class and merit in the Ameri­can high school: An assessment of the revisionist and meritocratic argu­ments. New York: Longman.

Rutter, M. ( 1983) School effects on pupil progress: Research findings and policy implications. Child Development, 54, 1-29.

Scarr, S. ( 1988). Race and gender as psychological variables: Social and ethical issues. American Psychologist, 43, 56-59.

Sewell, F. E., & Price, V. D. (1989, August). Motivation: 1mplicationsfor

267

enhancing learning and achievement of at-risk children. Paper present­ed at the annual meeting of the American Psychological Association, New Orleans, LA.

Snyder, T. D., & Hoffman, C. M. (1990). Digest of educational statistics, 1990. Washington, DC: National Center for Educational Statistics, U.S. Department of Education.

Steinberg, L., Dornbusch, S. M., & Brown B. B. (1992). Ethnic differences in adolescent achievement: An ecological perspective. American Psy­chologist, 47, 723-729.

Stem, J.D. (1987). The condition of education: Statistical report for edu­cational statistics. Washington, DC: U.S. Government Printing Office.

Uguroglu, M., & Walberg, H. J. (1986). Predicting achievement and moti­vation. Journal of Research and Development in Education. 19(3), 1-12.

Walberg, H. 1. (1981). A theory of educational productivity. In F. H. Far­ley & N. Gordon (Eds.), Psychology and education: The state of the union (pp. 36-77). Berkeley, CA: McCutchan.

Walberg, H. J. (1984). Improving the productivity of America's schools. Educational Leadership, 41(8), 19-30.

Walberg, H. J. (1986). Synthesis of research on teaching. In M. C. Wittrock (Ed.), Handbook of research on teaching (3rd ed., pp. 214-229). New York: Macmillan.

Walberg, H. J. (1990). Productivity & equity. In H. P. Baptiste, H. C. Wax­man, J. Walker de Felix, & J. E. Anderson (Eds.), Leadership, equity & school effectiveness (pp. 83-96). Newbury Park, CA: Sage.

Walberg, H. J., Fraser, B. J ., & Welch, W. W. (1986). A test of a model of educational productivity among senior high school students. The Jour­nal of Educational Research, 79, 133-139.

Wang, M. C. ( 1990). Programs that promote educational equity. In H. P. Baptiste, H. C. Waxman, 1. Walker de Felix, & J. E. Anderson (Eds.), Leadership, equity & school effectiveness (pp. 132-154). Newbury Park, CA: Sage.

Weiner, B. (1990). History of motivational research in education. Journal of Educational Psychology, 82, 616-622.

Wiley, D. E., & Hamischfeger, A. (1974). Explosion of a myth: Quantity of schooling and exposure to instruction, major educational vehicles. Educational Research. 79, 133-139.

Wolfle, L. M., & Ethington, C. A. (1986, April). Race and gender differ­ences in a causal model of mathematic achievement. In T. Z. Keith (chair), Methods and findings from longitudinal studies: The HSB har­vest rolls in. Symposium conducted at the annual meeting of the Amer­ican Educational Research Association, San Francisco.

APPENDIX

Variable

Family SES (1980)

Ethnicity (1980)

Gender ( 1980)

Ability (1980)

Quality of schooling (1982) Quality of education

Quality of teachers

School-wide discipline

Composite and Subcomposite Variables With Corresponding HSB Items and Value Labels

HSB items

Father's occupational status, mother's occupational status, father's education, family income, possession in the home.

Self report of race/ethnicity: Black, Hispanic, Native American, & other coded as non-Asian minority.

Sex of respondent.

Vocabulary standard score.

Student rates: (a) quality of instruction, (b) reputation of school in community, and (c) teacher interest in students.

Student rates number of teachers who (a) enjoy their work, (b) make clear presentations, (c) work students hard, (d) talk over students' heads (reverse scored), (e) are patient and understanding, (f) treat students with respect, (g) return work promptly, (h) are witty, and (i) are interested in students outside of class.

Student reports how often do students at school (a) talk back to teachers, (b) refuse instructions, (c) fight each other, (e) attack teachers, (f) cut class, or (g) don't attend classes?

Value labels

HSB composite developed from student report

0 Non-Asian minority I White or Asian

OMale

HSB Tscore

I Poor 3 Good

I None 3 Half

I Often 2 Sometimes 3 Never

I Female

2 Fair 4 Excellent

2AFew 4Most

(Appendix continues)

Dow

nloa

ded

by [

Uni

vers

ity o

f K

ent]

at 0

7:12

16

Nov

embe

r 20

14

268

Variable

Parental involvement ( 1982) Parental planning

Parental monitoring

Parent/school contact

Parental aspirations

Motivation (1982) Educational aspirations

Aspirations

Attendance

Preparation

School conduct

Interest

Perseverance

Locus of control

Coursework (1982)

The Journal of Educational Research

APPENDIX-( Continued) Composite and Subcomposite Variables With Corresponding HSB Items and Value Labels

HSB items

Did mother/father help plan student's after-high-school program?

My mother/father keeps track of school progress.

Student reports how often parents (a) attended PTA meetings, (b) attended parent­teacher conferences, (c) volunteered for school projects, or (d) called or saw teacher when there was a problem.

How far do your parents expect you to go in school?

Student plans to go to college; if so, how long after high school graduation?

Student plans to go to a 4-year college or university.

Student disappointed if doesn't go to college.

Do you have the ability to go to college?

How far do you plan to go in school? What level of schooling will you be satisfied with?

Student reports number of days (a) tardy in last half of year or (b) absent without illness in last half of year.

Student reports cutting class once in a while.

How often do you go to class without (a) pen or pencil, (b) homework, or (c) books?

Student reports having discipline problems in last year, being suspended for disciplinary reasons in last year, being suspended for academic reasons in last year.

I am interested in school. I like to work hard in school.

Student reports number of hours of homework completed each week.

Student agreement that (a) luck is more important than hard work in school, (b) when student tries to get ahead something or someone stops student, (c) planning leads to unhappiness as planning hardly ever works out, and (d) accepting conditions as they are is more likely to lead to happiness than trying to change things.

Have you completed (taken) the following courses: I st-year algebra, 2nd-year algebra, geometry, trigonometry, calculus, physics, chemistry, biology, honors English (Jr. or Sr.), honors math (Jr. or Sr.)?

Value labels

1 No 2 Some 3 A great deal

I False 2 True

1 Never 2 Once in a while 3 Often

I <H.S. 2 H.S. only 3 2-year college 4 4-year college 5 Master 6PhD/MD

1 No 2 Don't know 3 1+ years 4 1 year 5 Immediately

1 No 2Yes

1 False 2 True

I No 2 Doubt it 3 Not sure 4 Probably 5 Definitely

I Less than H.S. 2 H.S. only 3 2-year college 4 4-year college 5 Masters' 6PhD/MD

I 21 2 16-20 3 11-15 4 5-10 5 3-4 6 1-2 7 None

OTrue I False

I Usually 2 Often 3 Seldom 4 Never

OTrue I False

I False 2 True

00 I< I 2 1-3 3 3-5 45-10 5 > 10 6 15+

I Strongly agree 2Agree 3 Not sure 4 Disagree 5 Strongly disagree

ONo I Yes

Note. Date in parentheses following variable indicates survey year.

Dow

nloa

ded

by [

Uni

vers

ity o

f K

ent]

at 0

7:12

16

Nov

embe

r 20

14