neighborhood and school context, extracurricular

The Pennsylvania State University The Graduate School

College of Liberal Arts

NEIGHBORHOOD AND SCHOOL CONTEXT,

EXTRACURRICULAR PARTICIPATION,

AND

EDUCATIONAL OUTCOMES

A Thesis in

Sociology and Demography

by

Jason M. Smith

© 2006 Jason M. Smith

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

August 2006

The thesis of Jason M. Smith was reviewed and approved* by the following: _____________________________________

George Farkas Professor of Sociology and Demography Thesis Adviser Chair of Committee

_____________________________________ Barry Lee Professor of Sociology and Demography

_____________________________________ Suet-Ling Pong Associate Professor of Education and Sociology

_____________________________________ Roger Shouse Associate Professor of Education

_____________________________________ Paul Amato Professor of Sociology

Head of the Department of Sociology *Signatures are on file in the Graduate School.

ii

ABSTRACT

Participation in the extracurriculum is fairly well established as having positive effects on

educational outcomes, including grades, high school graduation, and postsecondary attainment. Furthermore, measures of disadvantage at the neighborhood and school levels have been shown to be associated with lower levels of achievement and attainment. However, the links between neighborhood and school contexts and participation in school activities has rarely been investigated, and the effects of extracurriculars have not been considered in light of neighborhood and school effects on educational outcomes, and have often been presented as monolithic. This study addresses both these gaps in the literature by employing a three-level analysis of students within neighborhoods within schools to answer two primary questions: 1) Do neighborhood and school context influence extracurricular participation? and 2) Does extracurricular participation affect educational attainment, controlling for these other influences? The answers appear to be 1) yes, especially for minority students; and 2) yes, for all outcomes and for nearly all measures of participation.

iii

TABLE OF CONTENTS List of Tables ……………………………………………………………………………............ vi List of Figures ……………………………………………………………………………...........vii Acknowledgements …………………………………………………………………………….viii Chapter 1. INTRODUCTION……………………………………………………………………. 1 Educational Outcomes and Extracurricular Activities...…………………………………………..2 Neighborhood and School Context………………………………………………………………..3 References ……………………….………………………………………………………………..7 Chapter 2. LITERATURE REVIEW……………………………………………………………...8 Neighborhoods and Education….…………………………………………………………………8 Neighborhood Effects Since Leventhal and Brooks-Gunn’s Review.….………………………..11 Methodological Approaches and Refinements.……………………………………………….....17 Stratification and Schools ……………………………………………………………………… 19 The Extracurriculum and Stratification .………………………………………………………...21 Hypotheses ………………………………………………………………………………………26 Summary ………………………………………………………………………………………...28 References ……………………….………………………………………………………………29 Chapter 3. DATA AND METHODS…………………………………………………………….34 Students within Neighborhoods within Schools ………………………………………………...34 The National Education Longitudinal Study (NELS) …………………………………………...34 Study Design and Construction of the Sample..............................................................................35 Operationalization of Key Concepts……………………………………………………………..39 School Measures…………………………………………………………………………………39 Neighborhood Measures…………………………………………………………………………40 Individual Level Measures……………………………………………………………………….41 Centering of Variables…………………………………………………………………………...43 Model, Analytic Strategy, and Study Hypotheses……………………………………………….45 A Three-Level Hierarchical Linear Model………………………………………………………45 Hypotheses……………………………………………………………………………………….49 Extracurricular Participation……………………………………………………………………..49 Educational Outcomes…………………………………………………………………………...51 Summary ………………………………………………………………………………………...51 References ……………………….………………………………………………………………52 Chapter 4. RESULTS…………………………………………………………………………….54 Extracurricular Participation……………………………………………………………………..56 Educational Attainment………………………………………………………………………….65 Summary and Review of Results………………………………………………………………...68 Tables………….…………………………………………………………………………………69

iv

TABLE OF CONTENTS (con’t)

Chapter 5. DISCUSSION ……………………………………………………………………….90 Does neighborhood and school context influence extracurricular participation?......................... 90 The Effects of Neighborhood Context….………………………………………………………..90 The Effects of School Context………….………………………………………………………..93 Does extracurricular participation affect educational achievement and attainment,

controlling for the influences of neighborhood and school context?................................ 98 The Effects of Extracurricular Participation on Educational Achievement and Attainment…….99 Contextual Effects on Educational Attainment…………………………………………………103 Conclusions……………………………………………………………………………………..107 References ……………………….……………………………………………………………..110

v

LIST OF TABLES Table 2.1: Neighborhood Effects Literature, Post-Leventhal-Brooks Gunn (1997)…………….12 Table 2.2: Extracurriculars Literature Summary………………………………………………...23 Table 2.3: Summary of Study Hypotheses ……………………………………………………...26 Table 3.1: List of Variables Used………………………………………………………………..40 Table 3.2: List of Equations……………………………………………………………………..47 Table 3.3: Summary of Study Hypotheses ……………………………………………………...50 Table 4.1 – Descriptive Statistics………………………………………………………………...69 Table 4.2: Summary of Study Hypotheses and Support / Non-Support for Each……………….70 Table 4.3 – Regression Coefficients for Odds of Any Extracurricular Participation …………...71 Table 4. 4 – Regression Coefficients for Total Extracurricular Participation…………………...72 Table 4.5 – Regression Coefficients for Odds of High Profile Sport Participation……………..73 Table 4.6 – Regression Coefficients for Odds of Low Profile Sport Participation……………...74 Table 4.7 – Regression Coefficients for Odds of Cheerleading / Pom Team Participation……..75 Table 4.8 – Regression Coefficients for Odds of Fine Arts Participation……………………….76 Table 4.9 – Regression Coefficients for Odds of Academic Club Participation………………...77 Table 4.10 – Regression Coefficients for Odds of Student Government Participation………….78 Table 4.11 – Regression Coefficients for Odds of Occupational Club Participation……………79 Table 4.12 – Regression Coefficients for Odds of Social Activity Participation………………..80 Table 4.13 – Regression Coefficients for GPA in 12th Grade…………………………………..81 Table 4.14 – Regression Coefficients for GPA in 12th Grade, with Extracurriculars…………...82 Table 4.15 – Regression Coefficients for Odds of High School Graduation…………………….83 Table 4.16 – Regression Coefficients for Odds of HS Graduation, with Extracurriculars………84 Table 4.17 – Regression Coefficients for Odds of Attending Postsecondary Education………..85 Table 4.18 – Regression Coefficients for Odds of Attending PSE, with Extracurriculars………86 Table 4.19 – Significant Interactions Between Neighborhood Racial Composition and

Individual Race on the Odds of Extracurricular Participation…………………………...87 Table 4.20 – Significant Interactions Between School Racial Composition and

Individual Race on the Odds of Extracurricular Participation…………………………...88

vi

LIST OF FIGURES Figure 1.1: Basic Conceptual Model……………………………………………………………...5 Figure 1.2: Nested Nature of NELS:88 Data, as used in this study……………………………….6 Figure 3.1: Nested Nature of NELS:88 Data, as used in this study……………………………...37 Figure 3.1b: Example of Cross-Classification…………………………………………………...38 Figure 3.2: Heuristic Model of Hypothesized Effects…………………………………………...46

vii

ACKNOWLEDGEMENTS I would like to thank the late Professor Alan G. Ingham for encouraging me to pursue a life as a scholar. I would not have come to Penn State and chosen this path without his support, knowledge, and faith in me. I would also like to thank the members of my committee, Suet-ling Pong, Roger Shouse, Barry Lee, and especially my committee chair, George Farkas. The guidance and advice the four of you have provided me has been invaluable, and I hope that my contributions as a scholar will someday prove worthy of all you have given me.

viii

DEDICATION

For my Father, John M. Smith, who always pushed me to be my best, never letting me settle for less.

For my Grandmother, Marilyn Everist, who always supported me in whatever endeavors

I pursued. And for my Mother, Becky A. Gonzalez, who passed away during the final stages of the

writing of this dissertation, and who always believed I could be anything I wanted, and made me believe it, too.

ix

INTRODUCTION

Social mobility is something sociologists, particularly those interested in education, have

studied for years, and yet much remains to be determined about the actual mechanisms by which

such mobility occurs. The most common path for improving one’s life chances is through college

attendance, and previous research (to be reviewed later) has shown that one factor that improves

educational attainment is participation in the extracurriculum during high school. Being involved in

school-sponsored activities has been shown to improve grades, school integration, standardized test

scores, graduation rates, and rates of postsecondary attendance; thus, it is obvious that participating

has desirable outcomes. One factor that may lead to these positive outcomes is the nature of the

relationships that result from involvement: participants surround themselves with motivated,

achievement-oriented peers, as well as connect themselves with adults who can provide guidance

towards desirable life paths.

But participation is not always a simple matter of individual choice. Context plays a part in

determining which activities are viewed as acceptable and worthwhile pursuits. Neighborhood and

school context can serve to deter students, particularly those from disadvantaged backgrounds, from

becoming involved in school-based activities, much as these students are deterred from formal

academic pursuits (Farkas, Lleras, and Maczuga 2002; Ogbu 1990). This discouragement has the

effect of denying to the student the potential benefits of participation and thereby the prospective

improvement in life chances that accompanies involvement. Such a contextual effect would

contribute to our understanding of how disadvantage in youth translates into disadvantage later in

life. To this end, this study investigates the following questions: 1) Do neighborhood and school

contexts influence extracurricular participation? and 2) Does extracurricular participation affect

educational attainment, controlling for these other influences?

1

Educational Attainment and Extracurricular Activities

Human capital theory suggests that increasing one’s education level is one way of effecting

upward mobility, and it has become well accepted in the literature that such self-investment does

indeed pay off. Attending college has become a common plan in the United States for improving

one’s human capital, but with the ever-increasing numbers of applicants, this path is becoming ever

more competitive and, hence, problematic as a means for upward mobility. (See (Beattie 2002) for a

discussion of how college attendance decisions are made.)

A recent book by Shulman abd owen (2001) examines the impact of athletics on admission

to “selective”1 colleges and universities. Their findings include the fact that, ceteris paribus, a

potential athlete has an advantage in the admissions process of as much as 48% over other equally

qualified applicants who are not potential athletes for the school – an advantage nearly double that

of a legacy, and nearly triple that of racial minorities.

These findings are consistent with previous research. Many studies have found that those

who participate in the extracurriculum do indeed have greater educational attainment (Hanks and

Eckland 1976; Howell, Miracle, and Rees 1984; Marsh 1993; McNeal 1995; Otto and Alwin 1977),

as well as better grades in high school (Broh 2002; Guest and Schneider 2003; Hanks and Eckland

1976) and improved labor market and occupational outcomes (Curtis, McTeer, and White 2003;

Eise and Ronan 2001; Picou, McCarter, and Howell 1985; Shulman and Bowen 2001). The primary

shortcoming of the existing research 2 is the monolithic definition of extracurricular activities as

“sports”, or only as sports and non-sports. This ignores the breadth and diversity of extracurricular

programs, which vary from the obvious high-profile sports (football, baseball, basketball) to fine

arts activities (drama clubs, music programs), and from academic clubs to social activities.

2

While Shulman and Bowen’s analysis takes the institution’s perspective as its point of

departure, the current analysis attempts to reverse the angle, looking instead from the bottom up:

What effect does extracurricular participation in high school have on one’s educational attainment?

If, as Shulman and Bowen found, athletes enjoy such a large admissions advantage, do participants

in other types of extracurricular activities see an advantage in admission and, thus, higher rates of

attendance? If so, which activities have this effect, how large is it, and for what groups does it

occur? These are particularly salient questions, since much work has been done in attempting to

understand the status attainment process, and extracurricular opportunities and participation are

easily addressed by policy actions. Practitioners can deal directly with this aspect of the attainment

process (Otto and Alwin 1977).

Neighborhood and School Context

As stated earlier, participation patterns are not simply a function of individual choice.

Contextual influences can play a part in determining the activities that are viewed as acceptable and

worthwhile pursuits. The neighborhood context and the school context are both important when

considering the cultural and normative milieu in which individuals learn and make decisions

regarding values, activities in which to partake, and behaviors that are acceptable and valuable.

Children from the same neighborhood (usually) attend the same school, play together, attend

community events with their families together, and so forth. In this way, norms of behavior and

expectations for the future are influenced by those around whom one lives (Eckert 1989; Macleod

1987; Willis 1977)

The implication here is that a student from a disadvantaged neighborhood is less encouraged

– perhaps even discouraged – and, hence, less inclined to be involved in school-related activities,

activities which could potentially be beneficial to one’s future. For example, if a student attends a

3

school that is higher in SES overall than his/her neighborhood, s/he is at an increased disadvantage

for educational attainment. The same can be said for race: minority students in mostly white schools

may be deterred from participation in certain activities, especially if they come from minority

neighborhoods. Thus, school and neighborhood can interact to defeat individual opportunities for

social mobility.

Beveridge and Catsambis have been among the first to simultaneously analyze school and

neighborhood contexts (Catsambis and Beveridge, 2001; Beveridge and Catsambis, 2002, 2003),

showing the influence of neighborhood on mathematics achievement in eighth grade, dropping out

of high school, and participation in risky behaviors. They do not, however, specify the

mechanism(s) by which these contexts operate on individual level outcomes. In other words, how

and why does neighborhood disadvantage produce deleterious outcomes? This study explores this

question by taking the position that one’s neighborhood is an important influence on the activities

one pursues, which in turn affect educational attainment and ultimately social mobility. A general

conception of the processes at work is presented in Figure 1.1.

Using data from the National Education Longitudinal Study 2000 follow-up, this research

analyzes the effects of student context (school, neighborhood of residence), background (race,

gender, and socioeconomic status), ability (through standardized test scores) and extracurricular

participation on a student’s grades, odds of graduating high school and of attending college. Using

ZIP Code identifiers contained in the Restricted NELS files, I append Census data will be appended

to student records to create a contextual data set for analysis using a three-level Hierarchical Linear

Model of students within neighborhoods within schools (see Figure 1.2). Extracurricular activities

include sports like football and basketball (which most people initially think of upon hearing

4

“extracurriculars”), as well as lower profile sports like lacrosse or field hockey, and non-athletic

activities like drama, journalism, and academic clubs. To review, the key questions of this research

are: 1) Do neighborhood and school contexts influence extracurricular participation; and 2) Does

extracurricular participation affect educational attainment controlling for these other influences?

5

REFERENCES

Beattie, Irene R. 2002. "Are All 'Adolescent Econometricians' Created Equal? Racial, Class, and Gender Differences in College Enrollment." Sociology of Education 75:19 - 43.

Broh, Beckett A. 2002. "Linking Extracurricular Programming to Academic Achievement: Who Benefits and Why?" Sociology of Education 75:69-95.

Curtis, J., W. McTeer, and P. White. 2003. "Do high school athletes earn more pay? Youth sport participation and earnings as an adult." Sociology of Sport Journal 20:60-76.

Eckert, Penelope. 1989. Jocks and Burnouts: Social categories and identity in the high school. New York: Teachers College Press.

Eise, R. and N. Ronan. 2001. "Is participation in high school athletics an investment or a consumption good?" Economics of Education Review 20:431-442.

Farkas, George, Christy Lleras, and Steve Maczuga. 2002. "Does Oppositional Culture Exist in Minority and Poverty Peer Groups?" American Sociological Review 67:148 -155.

Guest, Andrew and Barbara Schneider. 2003. "Adolescents' extracurricular participation in context: The mediating effects of schools, communities, and identity." Sociology of Education 76:89-109.

Hanks, M.P. and B.K. Eckland. 1976. "Athletics and Social Participation in the Educational Attainment Process." Sociology of Education 49:271-294.

Howell, F., A. Miracle, and C.R. Rees. 1984. "Do High School Athletics Pay? The Effects of Varsity Participation on Socioeconomic Attainment." Sociology of Sport Journal 1:15-25.

Macleod, Jay. 1987. Ain't No Makin' It: Aspirations and Attainment in a Low-Income Neighborhood. Boulder, CO: Westview Press.

Marsh, H. 1993. "The Effects of Participating in Sport during the Last Two Years of High School." Sociology of Sport Journal 10:18-43.

McNeal, Ralph B. 1995. "Extracurricular Activities and High School Dropouts." Sociology of Education 68:62-81.

Ogbu, John U. 1990. "Minority Education in Comparative Perspective." The Journal of Negro Education 59:45-57.

Otto, L.B. and D.F. Alwin. 1977. "Athletics, Aspirations, and Attainments." Sociology of Education 50:102-113.

Picou, J.S., V. McCarter, and F. Howell. 1985. "Do High School Athletics Pay? Some Further Evidence." Sociology of Sport Journal 2:72-76.

Shulman, James and William Bowen. 2001. The Game of Life: College Sports and Educational Values. Princeton, NJ: Princeton Press.

Willis, Paul. 1977. Learning to labor: How working class kids get working class jobs. New York: Columbia University Press.

1 Selectivity was based on the number of applications for admission greatly outnumbering the number of admission offers given. 2 More limitations of past studies are discussed in greater depth in Chapter 2.

7

LITERATURE REVIEW

In this chapter, I review the existing literatures that have informed the conceptualization

of this study. Neighborhood effects are reviewed first, setting the contextual stage and macro-

sociological picture of the settings within which students live. Next, I review student participation

in the extracurriculum and its effects on educational outcomes. The next chapter will detail the

operationalization of these concepts, and the specification of a model to estimate the hypothesized

effects.

Neighborhoods and Education.

The neighborhood effects literature is voluminous, covering such diverse outcomes as

health, crime rates, and residential satisfaction. Leventhal and Brooks-Gunn (2000) (hereafter,

LBG) provide a very thorough review of the literature covering outcomes relevant to children and

adolescents, separately reviewing studies of 1) school readiness and achievement, 2) behavioral

and emotional outcomes, and 3) sexuality and childbearing. This first group is of interest for the

present research, and will be briefly reviewed here. For a more extensive treatment, see LBG.

This review will then turn to neighborhood effects literature that has been done since the

publication of LBG.

LBG’s Table 1 (pp. 316-7, but not reproduced here) provides an overview of the literature

they review in terms of study design, sample, how neighborhood is defined and what data is used

to measure neighborhood constructs, and a brief summary of study findings. There are a dozen

different “Studies” listed in the table, and the review covers twenty-one published works using

data from these studies. The types of studies vary from intervention program data for low birth

weight and premature babies (The Infant Health and Development Program, IHDP) to nationally

representative, longitudinal surveys like the Panel Study of Income Dynamics (PSID). Findings

8

from other, more locally focused datasets like the Promoting Academic Competence (PAC) study

in Atlanta and the Woodlawn Study in Chicago were also reviewed. All of the studies reviewed in

the text and table reflect positive associations between what are generally understood to be

desirable outcomes and “better” neighborhoods, meaning higher SES, more affluent neighbors,

etc., and negative associations for disadvantaged neighborhoods. Certain studies from this review

are worth particular attention for the present research.

Brooks-Gunn and colleagues have done extensive work on the effects of neighborhoods,

particularly the negative effects of poor neighbors (Brooks-Gunn, Duncan, and Aber 1997a;

1997b; Brooks-Gunn, Duncan, Klebanov, and Sealand 1993; Klebanov, Brooks-Gunn, McCarton,

and McCormick 1998). These effects are found on outcomes including young and early school-

age children’s IQ, verbal ability, and reading recognition scores, and may be more important for

whites than for blacks. Neighborhood effects began to appear for children at around age 3.

Others have looked at the effects of neighborhood on adolescent outcomes (Dornbusch,

Ritter, and Steinberg 1991; Entwisle, Alexander, and Olson 1994; Halpern-Felsher, Connell,

Spencer, Aber, Duncan, Clifford, Crichlow, Usinger, Cole, Allen, and Seidman 1997). These

studies have found that adolescent achievement (in terms of math achievement, basic skills test

scores, and grade point average) is negatively related to neighborhood disadvantage, while

educational risk (of dropping out, failing high-stakes tests, etc.) increases as neighborhood

disadvantage goes up. The latter two studies (Entwisle, Alexander, and Olson 1994; Halpern-

Felsher et al. 1997) found that the effect of neighborhood SES is stronger for males than females.

In terms of educational attainment, studies have found that the odds of completing high

school, postsecondary attendance, and total years of schooling all have negative associations with

neighborhood disadvantage (Brooks-Gunn, Duncan, Klebanov, and Sealand 1993; Duncan 1994;

9

Halpern-Felsher et al. 1997). While these studies found that these effects were more consistent

and stronger for whites than blacks, Ensminger, Lamkin, and Jacobson (1996) used data on 954

African American males, and found the presence of middle class neighbors improved their odds

of graduation and years of schooling. The Gautreaux Study used a quasi-experimental design

where African American and Hispanic residents of public housing were afforded the opportunity

to move to more affluent suburbs, and Rosenbaum, Kulieke, and Rubinowitz (1988) showed these

youth had better school attendance, higher odds of being in college prep classes, and of

postsecondary attendance than youth who remained in the city. Using nationally and/or regionally

representative data, some studies have yielded results that suggest the link between neighborhood

disadvantage and attainment may be stronger for boys than girls (Duncan 1994; Ensminger,

Lamkin, and Jacobson 1996; Halpern-Felsher et al. 1997).

Other indicators of neighborhood SES (besides affluence of neighbors) also have been

found to have an association with educational attainment. The neighborhood high school dropout

rate, measures of female-headed households and female employment levels, as well as both the

number and proportion of managerial or professional workers are associated with the educational

attainment of neighborhood youth. These results were based on nationally representative data

(usually the PSID) (Aaronson 1997; Brooks-Gunn, Duncan, Klebanov, and Sealand 1993;

Duncan 1994; Ensminger, Lamkin, and Jacobson 1996; Garner 1991), all with the effects one

would expect (e.g., positive effects for number or proportion of professionals, negative effects for

the dropout rate.) An important analysis of this type was conducted by Crane (1991), who not

only reported an effect for the percentage of professional or managerial workers, but also found a

“tipping point,” below which the effect became more prominent: when a neighborhood had less

10

than 5% of its workers in these fields, the effect on the dropout rate for children from that

neighborhood increased.

Finally, racial/ethnic diversity has been found to be associated with both school readiness

attainment and school achievement. Living in a neighborhood with large proportions of Latinos

and/or foreign-born residents decreased young children’s verbal ability, an effect that was

stronger for whites than blacks (Chase-Lansdale, Gordon, Brooks-Gunn, and Klebanov 1997;

Chase-Lansdale and Gordon. 1996). However, among older adolescents, such neighborhood

diversity (Latinos and / or foreign-born residents) was associated with higher years of schooling

and college attendance for African American males (Duncan 1994; Halpern-Felsher et al. 1997).

Neighborhood Effects Research since Leventhal and Brooks-Gunn’s Review

As is apparent from Leventhal and Brooks-Gunn’s (1997) review, numerous datasets have

been employed to investigate the effects of neighborhood context on adolescent outcomes, with

neighborhood context being operationalized in a variety of ways. Much research has been

conducted since their review, still utilizing many different datasets and a variety of definitions of

“neighborhood.” (Table 1 summarizes the post LBG neighborhood effects literature, and uses the

same format as Table 1 in LBG.) Sampson et al (2002) review much of this work, and the

following discussion reflects much of their review.

As Sampson et al note, the most common way of defining neighborhood remains the

census tract, with data at this level of aggregation being appended to survey data from the PSID

(Aaronson 1998; Ginther, Haveman, and Wolfe 2000; Harding 2003; Vartanian 1999; Vartanian

and Gleason 1999), the National Education Longitudinal Study (NELS) (Blau, Lamb, Stearns,

and Pellerin 2001; Catsambis and Beveridge 2001), the Moving to Opportunity (MTO) study

11

Table 2.1: Neighborhood Effects Literature, Post-Leventhal-Brooks Gunn (1997)

Study Design Sample Nbhd Data Findings from published studies

PSID

Nationally representative longitudinal study

2609 children, born ‘62-‘72, followed from ‘68 (or birth) till ‘92

‘70 & ‘80 Census tract data

Aaronson (98): using fixed effects for families w/ kids 3+ yrs apart that move between nbhds, finds that nbhd effects are robust & persist in the face of indiv- & family-level controls, controls for family-unobservables, outcomes, estimation techniques, & samples

Ginther, Haveman, & Wolfe (00): more complete domain of family & individual characteristics reduces / eliminates many nbhd effects in magnitude & significance; closer the nbhd factor is tied to outcome measure, more likely it is to remain significant

Harding (2003): using counterfactual models, propensity score matching, & sensitivity analysis, when 2 groups of kids, identical at age 10 on observed variables, experience different nbhd contexts as adolescents, those in hi-poverty nbhds are more likely to drop out & to have a teenage pregnancy

South & Crowder (99): among black women, nbhd disadvantage has little impact on risk of premarital childbearing, but has a significant nonlinear effect on probability of marriage prior to first birth. Among white women, as nbhd disadvantage increases, premarital childbearing rates rise nonlinearly, & marriage rates rise linearly; white women’s estimated rates of premarital childbearing may approach those of blacks in the most disadvantaged nbhds; SES differences between the nbhds inhabited by black & white women explain only a modest proportion of racial differences in premarital childbearing & timing of first marriage.

Vartanian (99): African American women are highly affected by welfare use by their parents & White women are not. African American women are also highly affected by level of family income & county unemployment rate during childhood, but their likelihood of using welfare as adults is not affected by childhood nbhd conditions. White women are highly affected by their nbhd conditions during childhood.

Vartanian & Gleason (99): nbhd affects educ attainment among young people, but diff’ly by race: for blacks, living near wealthier neighbors, more 2 parent families, & more professional / managerial workers leads to a substantial decrease in HS dropout, but no effect on college graduation, & occur mainly among those from disadvantaged backgrounds; for whites, these conditions improve college graduation rates, but do not affect HS graduation rates, & occur mainly among those from relatively advantaged backgrounds

NELS: 88

Nationally representative longitudinal study

24,500 8th graders from 1052 schools

1990 Census tract data

Catsambis & Beveridge (01): nbhds w/ concentrated disadvantage & schools w/ high student poverty & absenteeism depress math achievement directly, & (for nbhds) indirectly by depressing parental practices associated w/ high math achievement; also, parents can counteract nbhd effects through frequent communicaton w/ , monitoring of, & providing extra opportunities to their children


Blau, et al (01): students who attend schools in nbhds in which there are no pronounced racial inequalities are likely to make gains in social studies; opportunities for social learning are superior when there are few racially confounded economic barriers

12

1990 Census ZIP code data

Beveridge & Catsambis (02): odds of dropping out affected by indiv educ experiences (academic performance, interest in school)), family life (SES, low family educational involvement), & nbhd characteristics (racial segregation, concentration of dropouts) more than by school characteristics; African Americans affected by racial composition of nbhd, especially high if live in Latino nbhds; nbhd mediated by family resources & parental activities


Ainsworth (02): strongest nbhd effects for high-status residents, then for residential stability; most strongly mediated through educ expectations & amount of HW completed (collective socialization), then by school climate, friends’ dropping out, & occup expectations – these mediators account for ~40% of nbhd effect

1990 PUMS (5%)

Nationally representative study

Latino children living with one / both parents

MSA/PMSA labor market char’tics

Landale & Lichter (97): Latino children nbhds w/ high unemployment, low wages, & substantial residential segregation at highest risk of poverty; labor mkt, parental employment, nor family structure can fully account for low poverty rate of Cuban kids nor high poverty rates of P Rican & Mexican kids

Moving to Opportunity (MTO)

Randomized design in 5 cities

Baltimore: 63 children from random sample of families


Ladd & Ludwig (97): only Sec. 8 vouchers that can be used in low-poverty areas improve educational opportunities of �rientat (w/o that restriction, families usually just move to other parts of the city, w/ schools that are little or no better than those serving public housing areas)

Comm’ties in Schools

Nonprobability sample of students identified as at-risk of school failure by school personnel in 9 states & D.C.

4772 middle & HS students, from 53 schools, 22 districts, & enrolled in dropout prevention programs

Based on student responses to nbhd conditions of social control, peer culture, & crime

Nash (02): Informal social control in a nbhd (indirectly) influences educational behavior (through its direct effect on sense of school coherence (the extent to which students perceive school as a comprehensible, manageable, & responsive environment). Thus informal social control is a protective factor. Nbhd crime is a risk factor, as it has “substantial negative direct effect on educational behavior that is not mediated by sense of school coherence.”

Project on Human Dev’t in Chicago Nbhds

Multi-stage probability sample representative of Chicago residents

8,782 residents of 343 NC’s

1990 Census Tracts aggregated into “Nbhd Clusters” (NC’s)

Sampson & Bartusch (98): �rientations toward law & deviance are rooted more in experiential diff’s associated w/ nbhd than in a racially-induced subcultural system; blacks actually less tolerant of crime than whites, but more cynical about legal system & dissatisfied w/ police

National Survey of Children

nationally representative survey of US children, age 7-11 in 1976, reinterviewed in 1981 & 1987

562 women who were in all 3 waves


South & Baumer (2000): over 1/3 of positive effect of nbhd disadvantage on premarital birth can be attributed to attitudes / behaviors of peers & to more tolerant attitudes toward unmarried parenthood; smaller proportion of the effect can be attributed to higher rates of residential mobility; adolescents’ educational aspirations, school attachment, & parental supervision do little to mediate

Nat’l Longitudinal Study of Adolescent Health

nationally representative longitudinal, school-based survey of adolescent boys & girls in grades 7-12

14,481 adolescent boys & girls

“Nbhd” = 1990 Census Tracts; “Community” = 1990 Census County Data

Billy, et al (2001): adolescent alcohol use (“binge drinking”) influenced by collective socialization (monitoring, adult role models), social & economic relative deprivation; however, most effects are smaller than individual-level effects, & most variables theorized to have effects do not

13

Community, Crime, & Health

probability sample of Illinois households

2,482 adults 1990 Census tract data

Ross, Reynolds, & Geis (00): nbhd residential stability In affluent nbhds associated w/ low levels of distress; under conditions of poverty the opposite is true; because residents of poor, stable nbhds face high levels of disorder in their nbhds; negative effects of poor, stable nbhds on residents’ psychological well-being do not stem from a lack of social ties among neighbors.

Ross (00): residents of poor, mother-only nbhds have higher levels of depression than residents of more advantaged nbhds:more than half of apparent contextual effect is really compositional, due residents of disadvantaged nbhds being disadvantaged themselves; however, a significant contextual effect survives; effects of female headship & poverty in the nbhd are mediated by perceived nbhd disorder

Ross, Mirowsky, & Pribesh (01): residents of disadvantaged nbhds have low levels of trust as a result of high levels of disorder in their nbhds: People who report living in nbhds w/ high levels of crime, vandalism, graffiti, danger, noise, & drugs are more mistrusting. The sense of powerlessness, which is common in such nbhds, amplifies the effect of nbhd disorder on mistrust

Ross & Mirowsky (01): residents of disadvantaged nbhds have worse health (worse self-reported health & physical functioning & more chronic conditions) than residents of more advantaged nbhds. The association is mediated entirely by perceived nbhd disorder & the resulting fear. It is not mediated by limitation of outdoor physical activity.

Youth Ach’ment & the Structure of Inner-city Comm’ties

representative sample of African American families with kids under 18 in Chicago

546 Af-American mothers with up to 2 children under 18, in both high-poverty, & middle class nbhds


Rankin & Quane (00): social-network composition & some forms of organizational participation are affected by nbhd poverty, decreasing the # of college-educated friends & employed friends, & increasing the # of friends on public aid; however, it increases odds of participating in community organizations, probably in efforts to counter the deleterious effects of disadvantage

1860 Census Manuscripts

Historical

boys with at least one parent for 2 cities Boston:18% of all households, rep’ing all parts of city in proportion to population Chicago: all families with kids 4-19 yrs

1860 Census Ward data: % household heads ofIrish nativity, & mean total wealth of families in ward

Galenson (97): in Boston in 1860, living in poor immigrant nbhd reduced odds of attending school for Irish immigrants’ sons; in Chicago in 1860, it increased the odds; in Boston, public school monopoly & nativist hostility at work; in Chicago, competition between public & parochial schools & aggressive building of schools in poor immigrant nbhds

Galenson (98): living in poor immigrant nbhd in Chicago in 1860 raised odds of attending school for native, German-immigrant, and Irish-immigrant sons (see above for why)

Non-Empirical Papers

-- -- --

Bauder (02): Nbhd effects research is ideologically based, b/c it assumes that certain things are inherently “pathological” or “dysfunctional” and ignores cultural differences and processes of marginalization that occur in society for those thatdeviate from “middle class norms”

Table Source: Pong, S.L. Unpublished manuscript.

14

(Ladd and Ludwig 1997), the Project on Human Development in Chicago Neighborhoods

(PHDCN) (Sampson and Bartusch 1998), the Community, Crime, and Health (CCH) survey in

Chicago (Ross 2000; Ross and Mirowsky 2001; Ross, Mirowsky, and Pribesh 2001; Ross,

Reynolds, and Geis 2000), the Youth Achievement and the Structure of Inner-City Communities

study in Chicago (Rankin and Quane 2000), and the National Longitudinal Study of Adolescent

Health (Add Health) (Billy, Cubbins, Grady, Kim, and Klepinger 2001; Pong and Hao 2004). The

other primary way neighborhood has been operationalized in the literature is the ZIP code of

residence, done with data from the National Survey of Children (NSC) (South and Baumer 2000),

and the NELS (Ainsworth 2002; Beveridge and Catsambis 2002). Other means of signifying

neighborhood in the recent literature include MSA / PMSA Census designations in conjunction

with PUMS data (Landale and Lichter 1997), self-reported “neighborhood” characteristics in the

Communities in Schools study (Nash 2002), and Census “ward” data from the 1800s in two

historical studies (Galenson 1997; Galenson 1998).

As Sampson, et al, discuss, this definition of neighborhood is not without its limitations.

People have many (if not most) of their experiences outside their “neighborhood of residence”, a

fact that is true even for children (Burton, Price-Spratlen, and Spencer 1997). Some scholars have

begun more intensive and elaborate means for operationalizing neighborhoods and the

characteristics of them that are salient for social research. Sampson et al discuss the efforts of

Grannis (1998; 2001), who employs the physical layout of street patterns to define “tertiary

communities” that encompass social networks and areas of social interaction. These areas

typically do not cross major thoroughfares, and more interaction occurs within them than crosses

busier streets.

15

Beyond the way neighborhood is defined, the list of specific characteristics of

neighborhoods that are hypothesized to have important effects in the recent literature has also

lengthened. Various factors related to SES, including (un)employment, education level of

residents, income, incidence of poverty, occupational level of neighbors, welfare receipt,

residential stability, informal social control, single parenthood, crime rates, immigrant status,

racial composition, and level of segregation have all been hypothesized to impact the residents of

a neighborhood. A common approach that has developed is to assess a neighborhood’s overall

“disadvantage” in some sort of scale or composite index, combining several of these factors. The

effects of these individual factors, or of a composite of them, are reviewed next.

The results from these studies are remarkably consistent, given the assortment of datasets,

the varied operationalization of the key concept (neighborhood), and the different predictors used.

Neighborhoods are found to affect educational outcomes including odds of dropping out of high

school (Beveridge and Catsambis 2002; Harding 2003; Vartanian and Gleason 1999); math,

reading, and/or social studies achievement (Ainsworth 2002; Blau, Lamb, Stearns, and Pellerin

2001; Catsambis and Beveridge 2001), odds of college attendance (Vartanian and Gleason 1999),

general educational opportunities (Ladd and Ludwig 1997), and sense of school coherence and

“educational behavior” (Nash 2002). Other outcomes related to youth that show effects of

neighborhood include teenage / premarital fertility and marriage rates (especially before a first

birth) (Harding 2003; South and Baumer 2000; South and Crowder 1999); odds of welfare use

(Vartanian 1999) and of being in poverty (Landale and Lichter 1997); orientations toward law

enforcement and deviance (Sampson and Bartusch 1998); adolescent alcohol use (Billy et al.

2001); social-network composition and organizational participation (Rankin and Quane 2000);

16

and various mental and physical health outcomes (Ross 2000; Ross and Mirowsky 2001; Ross,

Mirowsky, and Pribesh 2001; Ross, Reynolds, and Geis 2000).

In all cases, neighborhood disadvantage – whether measured with particular indicators or

a composite of several – has effects that increase “negative” or reduce “desirable” outcomes, as

usually understood. Some groups are affected more than others by certain conditions, or certain

conditions may affect people differently depending on individual and family circumstances (see

Table 1 for more detail), but, generally speaking, undesirable conditions in one’s neighborhood

lead to undesirable outcomes. This raises two points of criticism. One is the point addressed by

Bauder (2002), that neighborhood effects research is ideologically based, because it assumes that

certain things are inherently "pathological" or "dysfunctional." Bauder (2002) critiques this line

of research, saying it ignores cultural differences and processes of marginalization that occur in

society for those that deviate from "middle class norms". Others critique the results above by

raising the questions of selection into particular neighborhoods and (relatedly) the confounding of

individual-level attributes with neighborhood effects. Efforts have been made to address these

concerns through more careful methodology.

Methodological Approaches and Refinements

Beyond the different definitions of neighborhood, analytic approaches to the topic of

neighborhood effects have also become more diverse, though regression (and the related method

of multilevel modeling) remains the primary method of inquiry. In an effort to address the

confounding of neighborhood effects with the decisions families make to live in a particular

neighborhood, Aaronson (1998) used fixed effects models to examine the outcome of families

moving between neighborhoods. He found that neighborhood effects are robust, and persist in the

face of individual- and family-level controls, controls for family-unobservables, outcomes,

17

estimation techniques, and samples. Ginther, Haveman, and Wolfe (2000) found that a more

complete domain of family and individual characteristics reduces or eliminates many

neighborhood effects in magnitude and significance. However, they also found that the closer the

neighborhood factor is tied to the outcome measure, the more likely it is to remain significant,

emphasizing the importance of a sound theoretical basis for testing a particular model.

Furthermore, Harding (2003) used counterfactual models, propensity score matching, and

sensitivity analysis, to attempt to deal with potential biases and misspecification of neighborhood

models. He found that when two groups of children, who were identical at age 10 on observed

variables, experience different neighborhood contexts as adolescents, those in high-poverty

neighborhoods are more likely to drop out and to have a teenage pregnancy.

Sampson et al (2002) also encourage the inclusion of a spatial perspective when analyzing

neighborhood effects. This means making consideration of the fact that a neighborhood, however

defined, is not an insular unit. Other areas that bound a given neighborhood can have strong

effects on the types of activities that occur in and around that neighborhood, as demonstrated by

Morenoff (2001), Smith et al (2000), and others. Techniques like spatial regression and network

analysis are being applied in order to handle this “spatial dependency” exhibited by

neighborhoods, and provide a useful and important new direction in neighborhood effects

research.

In sum, the neighborhood effects literature is extensive, and continues to grow in terms of

use of datasets, definitions of neighborhood, the breadth of outcomes tested, and methodological

rigor. The educational outcomes of youth are clearly linked with various measures of

neighborhood disadvantage, most prominently the type of neighbors they have (in terms of

occupational status, (un)employment, and income level), aggregate measures of educational

18

attainment, the proportion of female-headed households, and racial / ethnic diversity (including

proportions of foreign-born residents).

Relative to this body of work, the contribution of the present research is to examine

whether neighborhoods affect the activities in which adolescents do and do not participate, and

how these activities subsequently affect educational attainment. In this manner, the mechanisms

by which neighborhood affects life chances and outcomes are more clearly delineated. The

measures to be used in the current research are detailed in the following (Data and Methods)

chapter.

Stratification and Schools

Schools have long been criticized in sociological circles for reproducing the inequalities

of society (see, for example, Bowles and Gintis (1976; 1987) and Vanfossen, Jones, and Spade

(1987)). In their recent follow-up article to Schooling in Capitalist America, Bowles and Gintis

(2002) revisit the idea of intergenerational transmission of income, finding that the probability

that a son from the poorest decile has a 19% chance of remaining in the poorest decile as an adult,

while the richest son has a 22% chance of remaining in that decile as an adult. Meanwhile the

chances of a son from the poorest decile moving up to the richest are only 1%, and there is no

chance (0%) that a son from the richest decile will fall to the poorest.

Such relative immobility of the vast majority of Americans is well documented, but the

stratification literature often has little to say about the actual mechanisms by which mobility (or

the lack thereof) occurs. Bowles and Gintis’ (2002) argument, as it relates to schools, is that

cognitive ability is part of a set of “personal capabilities” which play a role in one’s earnings later

in life, and that this cognitive ability is only a part of what schools affect.

19

One characteristic of schools that is an important consideration is school size. While the

“small schools” movement has gained momentum in the last decade or so, arguments for the

large, comprehensive high school remain powerful, and for good reason. James Conant was

perhaps the most persuasive and famous proponent of this mode of organization for public

schools (c.f.Conant 1959; 1964). With the recent push for smaller schools, debate over the

appropriate size of a student body has regained vigor. In a discussion of how the large high school

develops desirable character traits in students, Shouse (2004) specifically discusses the

extracurriculum and its contributions to such development. He notes that because of their larger

student bodies, comprehensive high schools are able to provide more “informal, semiformal, and

formal experiences” (p. 70) which offer opportunities for the development of “democratic spirit.”

Shouse goes on to say, “For example, extracurricular activities, though part of the formal

organization of schools, offer students wide opportunity for informal and semistructured

interaction and engagement” (pp. 72-3). He likens the large high school to a small city, noting

that it “will tend to offer a greater number of opportunities for students to gain social status, a

wider array of courses, athletic activities, clubs, and a multiplicity of social groups or ‘cliques’

with which may identify” (p. 78). Thus, school size is an important consideration for the effects

of schools on outcomes that reflect integration to and participation within the school.

The effects of schooling on the social world of the adolescent have a long history of

sociological inquiry, most famously beginning with Coleman’s (1961) seminal work, The

Adolescent Society, but going back even further to Hollingshead’s (1949) Elmtown’s Youth, upon

which Coleman drew heavily. Willis’ (1977) Learning to Labor and, more recently, Eckert’s

(1989) Jocks and Burnouts continued this line of inquiry, examining how adolescents’ peer

associations affect opportunities, aspirations, and life trajectories. As these works show, the social

20

relationships adolescents hold have tremendous impact on them, their lives, and how they view

their world and place in it. Indeed, Harris (1998) argues that it is the relations between youth and

their peers that are most important in the socialization of youth, overriding even parental

influence. It seems obvious, then, that the social relationships students form will have direct

impact on the choices they make regarding activities to pursue and the paths their lives

subsequently take.

The Extracurriculum and Stratification

One domain of choices adolescents make that is a possible mechanism for mobility and

that has garnered renewed interest of late in the sociological literature, is the extracurriculum.

Going back to Coleman (1961), the activities schools provide students beyond the classroom have

long been of interest to social scientists, but recently sociological inquiry into the effects of

extracurricular participation has expanded. Numerous studies have been performed, generally

affirming the fact that participants in the extracurriculum, be it sports or non-athletic

programming, have better educational and occupational outcomes, defined variously as grades,

graduation rates, dropout rates, earnings, employment rates, and so forth.

Past studies have shown that participation in the extracurriculum has positive effects on

educational aspirations (Marsh 1993; Otto and Alwin 1977), educational attainment (Hanks and

Eckland 1976; Howell, Miracle, and Rees 1984; Marsh 1993; Otto 1976; Otto and Alwin 1977),

occupational aspirations (Otto and Alwin 1977), occupational attainment (Marsh 1993; Otto

1976; Otto and Alwin 1977), and earnings (Howell, Miracle, and Rees 1984; Otto 1976; Picou,

McCarter, and Howell 1985). More recently, the resurgence of interest in the extracurriculum has

replicated these findings (Barron, Ewing, and Waddell. 2000; Curtis, McTeer, and White 2003;

Eise and Ronan 2001; Shulman and Bowen 2001). McNeal (1995) and Mahoney (2000) also

21

showed that participation increases the odds of completing (i.e., not dropping out of) high school,

while Eccles and Barber (1999) found that participation in extracurricular activities decreased

risk-taking behavior. Table 1 summarizes the previous literature on extracurricular participation.

What is there about participation in extracurricular activities that explains these findings?

Extracurricular participation can be a place for learning skills (e.g., teamwork, goal formation,

and so on – human capital), and for forming relationships (social capital) that surround a student

with peers and adults who foster mobility. But few studies have employed this theoretical

orientation to explain the mechanism by which these favorable outcomes are obtained. Guest and

Schneider (2003) employed both community / school contexts and individual identity to explain

the beneficial outcomes of participation in sports and non-sports on both achievement and

ambition. Curtis, McTeer, and White (2003) used the concepts of capital in their “General

Theoretical Interpretations” section, but did not include them empirically in their analyses.

Social capital may be the mechanism for the positive outcomes of extracurricular participation, as

shown in the study by Broh (2002). She shows that participation generally increases one’s stock

of social capital, and that these explain much of the positive effect of participation on math and

English grades in 12th grade. Broh notes that social capital is both a mechanism for social control,

“promoting compliance and trust among group members (Hirschi 1969) ” (p. 72-3), as well as

information and resource dissemination, provided that the “actors involved in the interaction must

(1) have human capital or an educational resource to transmit, (2) be willing to share these

resources (see (Portes 1998), (3) engage in an education-related interaction (e.g., parents talk to

each other about educational issues at a sports event), and (4) use any resource obtained.” (p. 73).

In addition to the more developed theoretical and empirical incorporation of social capital, Broh

(2002) also goes beyond the usual sports / non-sports dichotomy to decompose participation

22

Table 2.2: Extracurriculars Literature Summary

Authors and Year Dependent Variable Xcurr Effect Notes

Otto educational attainment + athletics only (1976) occupational attainment + income + Hanks & Eckland grades 0, + effects of athletics, "social participation" listed (1976) curriculum track 0, + educational attainment 0, + Otto and Alwin educational aspirations + effects occur primarily through significant others (1977) occupational aspirations + educational attainment + occupational attainment + McNeal dropping out - effect holds for athletics or fine arts participation when each is taken (1995) individually; when taken together, only athletics effect is significant Howell, et al educational attainment + (1984) wages 0 perhaps not enough time had passed for effect to manifest Picou, et al (1985) wages / income + white males only Marsh educational aspirations + (1993) college attendance + employment 0 Sabo (1993) postsecondary attainment + , 0 for white males, suburban white and rural Hispanic females; for blacks Eccles & Barber (1999) risk-taking behavior - Barron, et al (2000) educational attainment + males only; effects are stronger for those whose participation was "intensive" employment 0 wages + Mahoney (2000) completing High School + Eise & Ronan (2001) earnings + for black males only (after controls) Shulman & Bowen (2001) earnings + for high school athletes who attended college Broh 12th Grade Math Grades + for interscholastic sports, music, student council, and voc ed (2002) 0 for cheerleading, drama, yearbook / journalism 12th Grade Eng Grades + for interschool sports,music,drama, student council,yearbook/journalism, voc ed 0 for cheerleading 12th Gr Math & Eng Grades - for intramural sports Curtis,et al, (2003) earnings + Guest & Schneider achievement + in low- and middle-SES community schools only; (2003) educational expectations + zero or negative in high SES areas

into various categories, including interscholastic sports vs. intramural, music groups, drama,

student council, journalism, and vocational clubs. This sort of breakdown of activities was first

employed (in similar, but slightly different form) in McNeal’s (1995) analysis of the effects of

participation on dropout rates. Different types of activities have different demographics and,

23

therefore, differential social capital available to participants. This differential context will cause

various activities to have different effects on participants and their outcomes.

As evidenced by the more theoretically developed approaches of McNeal (1995), Broh

(2002), Curtis, McTeer, and White (2003), and Guest and Schneider (2003), the approach social

scientists are taking in order to understand the effects of extracurricular participation is

improving. However, other shortcomings exist in most of the literature that have not been

addressed fully. Most of the previous studies produced results that were not generalizable to the

entire high school population, since the data were often regional, and are now quite dated.

Furthermore, many of the above studies only included males.

There are numerous issues concerning these characteristics of the extant literature.

College and other post-secondary education have come to be a much more necessary part of

occupational attainment with the restructuring of the US economy of the last 35 years. Doors

have been opened by Affirmative Action and other legislation for many more people to attend

higher education. In addition, the athletic enterprise has become much more important and

influential in college admissions, thus having been a high school athlete in the last 15 to 20 years

is more important than in the early samples studied by previous literature (see Shulman and

Bowen (2001) for an extensive and enlightening discussion of this phenomenon). Furthermore,

females have seen especially sweeping changes in the structure of sport, and the outcomes for

them may be especially different than in the past. Female athletic opportunities were rather

limited until the 1970s with the advent of Title IX in 1972, and even for years after that as

institutions failed to implement changes required by that law. Not until the late 1970s did the

government even begin to pursue seriously enforcement of the statute. Rates of participation, and

24

the lower level of intensity of what participation there was, made it unlikely that any effects for

females would exist or be significant (statistically or substantively.)

Additionally, with the exception of Broh (2002) and McNeal (1995), the aforementioned

studies only consider extracurriculars in terms of sports, or only compare sports with non-sports,

ignoring the differences between, for example, vocational clubs and school music groups. Few of

these studies explicitly include other activities like drama, journalism, or music, and when they do

these activities usually are considered as an aggregate – extracurriculars in general, or only

differentiating between “sport” and “non-sport” activities. As already noted, different types of

activities have different demographics and, therefore, differential social influences on

participants. Therefore, they may have different effects, and aggregating them all simply as

“extracurriculars” can mask the effects of certain endeavors or make others look more (or less)

facilitating than they are.

Clearly, research with more recent data from nationally representative samples which

includes controls for various demographic characteristics and the effects of different types of

activities is needed. Furthermore, with the advances of women in the realm of sport, as well as

higher education and the labor force, incorporation of females is requisite when attempting to

quantify the effects of participation for student outcomes. Such research will be more convincing

for policy purposes, giving decision makers direct evidence of programs and activities that can

most assist high schoolers in improving their life chances. To begin to fill these gaps in the

literature, this study employs a nationally-representative dataset collected between 1988 and

2000, that includes both males and females, subdivides participation into more specific categories

than “sport vs. non-sport,” and indicates any postsecondary attendance at any time in the

approximately eight years after high school.

25

Hypotheses

Based on previous research, I generate and test several hypotheses, which are summarized

in Table 2.3. Two sets of hypotheses are presented, one for extracurricular participation and one

for educational outcomes.

Table 2.3: Summary of Study Hypotheses

Extracurricular Participation H1: Ethnic minority and students with family structure other than two biological parents

will have lower participation in all categories of extracurriculars. (Eq. 1)

H2: Students of higher SES and Females will have higher participation in all measures of extracurriculars. (Eq. 1)

H3: Neighborhood disadvantage will lead to lower rates of participation in all measures of extracurriculars. (Eq. 2)

H4: Neighborhood race will make more positive the effect of the corresponding individual level race, and will make more negative the effect of different individual race (interaction effects). (Eq. 3)

H5: School race variables will make more positive the effect of the corresponding individual level race, and will make more negative the effect of different individual race (interaction effects). (Eq. 5)

Educational Outcomes (all Eq.6) H6: Extracurricular participation will increase 12th grade GPA.

H7: Extracurricular participation will increase odds of graduating from high school.

H8: Extracurricular participation will increase odds of attending postsecondary education, except participation in Occupational Activities (which will decrease these odds).

Extracurricular Participation

Racial and ethnic minority students are usually less integrated with their schools (Hanks and

Eckland 1976; Smith 2003), particularly in schools where a relatively large proportion of the

student body is white (Farkas, Lleras, and Maczuga 2002). Thus, Hypothesis 1 (H1) predicts

minorities to be less involved in the extracurriculum than whites, while H5 says this effect will be

less substantial in schools where the student is not as isolated in terms of race / ethnicity.

26

Alternative family structures often provide fewer resources – economic, social, emotional, time,

etc. – to support involvement in activities (Downey, 1995; Mclanahan and Sandefur 1994; Powell

and Steelman 1990). Thus students from non-biological two-parent families will also have lower

rates of participation.

Conversely, students from higher-SES backgrounds have greater resources, which

facilitate their involvement in extracurriculars, as does the achievement orientation characteristic

of parents and students from such backgrounds. Females have also been shown to have higher

rates of participation (Smith 2003). Thus H2 predicts higher rates for these groups.

Because of the sparse resources, and oppositional culture (Farkas, Lleras, and Maczuga 2002;

Ogbu 1990) associated with disadvantaged neighborhoods, students from such areas will in

general be less able and less inclined to be involved (H3), while minority and “broken family”

students from such neighborhoods face a kind of “double disadvantage” that further reduces their

rates of participation (H4). Hypotheses 4 and 5 predict that living in a “minority neighborhood” or

attending a “minority school” will change the effect of being a minority student. Being a minority

is expected to produce lower odds of participation, and if that minority student lives in a minority

neighborhood or attends a minority school, those odds will be reduced further. However, living in

a co-ethnic neighborhood should integrate the student into their school and community more

effectively (Pong and Hao 2006), thereby raising (or making less negative) the odds of

participation. Because a student in such an environment may feel less alienated from the school,

participation should be more likely.

Educational Attainment

As shown in previous research, participating in the extracurriculum benefits educational

outcomes (H5, H6 and H7) (Broh 2002; Furstenberg and Hughes 1995; Hanks and Eckland 1976;

27

Howell, Miracle, and Rees 1984; McNeal 1995; Smith 2003). The only addendum that needs be

made to these hypotheses regards Hypothesis 7. Occupational Activities include things like

Future Farmers of America and vocational education programs, which are geared towards

immediate employment after high school. However, this category also includes Journalism /

Yearbook programs, Future Teachers of America, etc., which may lead to college attendance in

order to complete preparation for entry into such careers, though not necessarily. For these

reasons, participation in Occupational Activities is hypothesized to have negative effects on

postsecondary attendance.

SUMMARY

The literature reviewed in this chapter presents a picture of the contexts of students’ lives,

suggesting how the neighborhood in which a student lives and the activities in which he or she

participates can influence one’s future. Neighborhood disadvantage serves to depress educational

attainment through a variety of factors, most of which are associated with the socioeconomic

level of that neighborhood, while participating in extracurricular activities generally serves to

improve educational outcomes. The literature on the social structure of schools (and of lives in

general) suggests that students’ involvement in the extracurriculum can be influenced by their

social context. This research will test eight hypotheses regarding whether one’s neighborhood of

residence affects rates of involvement in the extracurriculum, and whether these subsequently

affect educational attainment. The measures to be used, and the multilevel statistical model

estimating the effects associated with them, are detailed in the following chapter.

28

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33

DATA AND METHODS

The previous two chapters have outlined the substantive and theoretical reasoning for

undertaking this study. Through discussion of the previous literature, I have outlined the

conceptual framework for analyzing the effects of neighborhood context on extracurricular

participation, and of each of these (controlling for individual background and school

characteristics) on educational attainment. In this chapter, I review the analytic approach for the

estimation of this model and the conceptual measures used therein.

Students Within Neighborhoods Within Schools

To address aforementioned shortcomings in prior research on extracurricular activities,

and to include measures of neighborhood conditions that influence educational outcomes, the

present research will utilize a recent, nationally representative dataset, the National Education

Longitudinal Study (NELS), with data from 1988 through 2000.

The National Education Longitudinal Study (NELS)

The NELS is a clustered, stratified national probability sample of 1,052 public and

private schools with an 8th-grade. The base-year survey for NELS:88 was carried out during the

1988 spring semester, when nearly 25,000 students were surveyed. On average, 23 student

respondents represented each of the participating schools (Curtin, Ingels, Wu, and Heuer 2002).

This nationally representative sample of students was followed from the eighth grade (in

1988) until 1994, two years after high school graduation (or what should have been their year of

graduation, in the case of those who did not finish on time or at all.) Over 12,000 of them were

resurveyed again in 2000, with further updates to their educational attainment, work histories,

and earnings histories (amongst other topics) added to the existing data. Information on student

background (race, gender, SES, family structure) and extracurricular participation in a range of

34

activities is available (detailed below), as well as school characteristics and the results of a

standardized test given specifically for the NELS. The restricted-use version of the NELS data

contains the student’s ZIP code of residence at each survey point, allowing the creation of

contextual datasets for studying the effects of neighborhood on student outcomes. The dataset

also includes student grades, whether or not the student graduated from high school, and what (if

any) postsecondary institution they attended, the outcomes of this analysis. Linear regression of

grades and logistic regression of the latter two dependent variables (odds of graduation and of

postsecondary attendance) will be estimated on the independent variables of interest, with SES,

race, gender, family structure, and student standardized test scores as controls.

Study Design and Construction of the Sample

Because of the repeated survey structure of the NELS, I am able to employ a longitudinal

design in the present study. I will use information from the 10th grade wave of surveys (collected

in the “First Follow-Up” in 1990), including neighborhood measures, extracurricular

participation, and individual background information, to predict educational outcomes two or

more years later, including grades in 12th grade, odds of graduating from high school, and the

odds of attending some form of postsecondary education (PSE).

The full 2000 NELS sample (n=12,144) was reduced to include only those students who

had not dropped out before 10th grade (the starting point of this analysis), leaving a sample of

n=11,191, and I then eliminated students with no data for their ZIP code of residence, making the

available sample n=10,714. Because I am estimating a model that includes school effects, I kept

in the sample only students who were in the same school between 10th and 12th grades, leaving a

sample of n=9,155. This sample construction process reduced the proportions of both Hispanic

and black students represented in the sample, as well as raising the overall SES level of the

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sample. This is not unexpected, as these populations (racial / ethnic minorities and students of

lower social class) are overrepresented among students who drop out of high school. The initial

sample consisted of 13.1% Hisnpanic and 9.5% black students, and had a mean SES of -.036 (see

the explanation of the SES measure below). The sample of 9,155 had an average SES level of

.046 (an increase of approximately 1/10 of a standard deviation) and included 11.5% Hispanic

and 8.9% black students. The proportion of students who are female rose from an initial 52.1%

to 52.3%, and the average standardized test score rose from 50.89 to 52.46. Again, these changes

are to be expected, as females are underrepresented among dropouts, and students with the

lowest levels of achievement drop out at higher rates as well. To illustrate, among the students

who dropped out before the tenth grade, and that were subsequently dropped from the sample for

this study, over 21% were Hispanic, nearly 11% were black, over 54% were male, with average

test scores of 30.5 and SES score of -.736.

Analyses of NELS data must control for the nested nature of the sample. That is, students

were not selected completely at random, but, instead, were selected from within schools that had

been selected at random. In other words, students are “nested” within schools. Students in the

same school may resemble each other more than chance would allow. Furthermore, students in

the same school may come from the same neighborhood, and the same “clustering” may be at

work, making students from the same neighborhood more alike than students selected

completely at random.

Given the focus on neighborhood and school effects in this research, this is a particularly

important point, and yields three levels of analysis: students within neighborhoods within

schools, as illustrated in Figure 3.1. This may seem counterintuitive (schools are thought to be in

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neighborhoods), but high schools have large catchment areas, which can cover multiple ZIP

codes, particularly in urban areas. Students from a particular neighborhood, and others from

other neighborhoods, all attend one school, thus it is the neighborhood level that is nested within

the school.

One problem for investigating neighborhood effects using the NELS is known as “cross-

classification.” That is, some neighborhoods may send their students to more than one school.

Figure 3.1b illustrates this situation, where the bold lines designate a student who lives in a

neighborhood with three other sample members, but attends a different school than the other

three. This situation arises because the neighborhood was not used as a sampling unit

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in the creation of the NELS sample. Rather, schools were selected from a national sampling

frame, and then students were selected from within each school.

To rectify this problem, I examined the number of students from each neighborhood in

each school. I retained the students that attended the school receiving the largest number of

students from that neighborhood. In other words, if Neighborhood X had five students in the

NELS, and Students A, B, and C attended School 1, while Students D and E attended School 2,

Students D and E were dropped from the sample for my analyses. In some cases, there were

equal numbers of students attending different schools. In this case, I kept the group of students

(grouped by school) with the least amount of missing data. After eliminating these cross-

38

classified cases, the sample was reduced from the aforementioned 9,155 to a final sample of n=

8,346. Little or no differences exist between the sample members retained and those dropped

from the sample. To maintain the integrity of this final sample and maximize the available

information for analysis, I imputed values for any missing data by using the expectation

maximization method for missing data imputation available in SPSS statistical software. (See

Chapter 4 for a discussion of how the sample construction process affected outcome measures

for the sample.)

Operationalization of Key Concepts

As a result of the nested nature of the data, analyses must account for non-independence

of observations. This will be accomplished through the use of a three-level hierarchical linear

model, estimated in the HLM statistical software package. The variables to be included at each

level of analysis are reviewed below, and summarized in Table 3.1.

School Measures

Research conducted by Beveridge and Catsambis (Beveridge and Catsambis 2002, 2003;

Catsambis and Beveridge 2001) has investigated three-level models of neighborhood and school

effects on risky behavior, high school dropout and math achievement, using the NELS data.

Their analyses include measures of the percentage of students on free- or reduced-price lunch

within the school (a measure of the overall SES level of the school), urbanicity (dummies for

urban and rural schools, with suburban schools as the reference group), and the percent minority

of the student body. These will be used in this study as well, but instead of percent minority, I

will use the more specific racial delineations of percent of 10th grade students who are black,

Hispanic, and Asian in the school (with percent white as the reference category). I will also

include an indicator for private schools, the total enrollment of the school, the percentage of

39

Table 3.1: List of Variables Used

School Variables Urban School (1=Yes)

Rural School (1=Yes)

Private School (1=Yes)

Total Enrollment

% of 10th Graders Asian

% of 10th Graders Hispanic

% of 10th Graders Black

% Single Parent

% LEP

% on Free Lunch

Neighborhood Variables % of Residents Black

% of Residents Asian

% of Residents Hispanic

% of Residents Foreign Born

% of Households Female-Headed

Economic Distress

Individual Variables SES RACE (1=Yes)

Asian

Hispanic

Black

(reference category = White)

FEMALE (1=Yes) FAMILY STRUCTURE (1=Yes)

Single Parent Family

Other Family Form

(reference category = 2 parent family)

Test Scores

EXTRACURRICULARS Any Participation? (1=Yes) Total Participation CATEGORIES (1=Yes) Hi Profile Sports

Low Profile Sports

Cheerleading / Pom

Fine Arts

Academic Clubs

Student Government

Occupational Clubs

Social Activities

students who come from single-parent homes, and the percentage of students who are of limited

English proficiency (LEP). All of these measures are taken from the School Administrator

Questionnaire portion of the survey recorded during the first follow-up, when students were in

10th grade.

Neighborhood Measures

Measures of relevant neighborhood constructs have been appended to the NELS data.

These data are drawn from the 1990 US Census, and are aggregated at the ZIP code level.

Following the prior literature (e.g., (Aaronson 1998; Brooks-Gunn, Duncan, and Aber 1997a;

Brooks-Gunn, Duncan, and Aber 1997b; Brooks-Gunn, Duncan, Klebanov, and Sealand 1993;

Chase-Lansdale and Gordon. 1996; Crane 1991; Duncan 1994), I include the following

40

measures: the percentage of residents who are black, Latino, and Asian, and the percentage who

are foreign born; the percentage of neighborhood households that are female-headed; and a

composite measure of “economic distress.” This composite is the average of the z-scores for the

neighborhood unemployment rate and the percentage of families with incomes less than $9500

per year (very poor families). Higher scores on any and each of these measures are defined in

this study as higher levels of “disadvantage”.

Individual-Level Measures

To adjust for student and family background, a number of control variables will be

included in all analyses: student race (in a series of dummy variables for black, Asian, and

Hispanic, with white as the reference category); gender (Female=1); SES (a composite variable

created within the NELS dataset from father's education level, mother's education level, father's

occupation, mother's occupation, and family income); family structure (dummy variables for

single-parent family [mother or father], and other family forms [other relative or non-relative],

with two-parent families (including step-parent families), as the reference category); and score

on a standardized achievement test given specifically for the NELS. These variables control for

such selection factors as academic ability and available financial resources that may facilitate

educational attainment independently of the key independent variables, extracurricular

participation and the neighborhood measures.

Following Broh (2002) and McNeal (1995), I operationalize extracurricular activities in a

more complete manner, using (in turn) general extracurricular participation (“Did the student

participate in any activity?” Yes=1); total number of extracurriculars participated in; and

categorical extracurricular participation, all taken from the 10th grade (first followup) survey in

1990. The categories of activities are High Profile Sports1 (interscholastic football, basketball,

41

and/or baseball or softball for females); Low Profile Sports (any other sport, either team or

individual; e.g., soccer, swimming, etc.); Cheerleading / Drill Team / PomPom Squad; Fine Arts

(any sort of band, choir or drama group); Academic Activities (academic clubs, honor societies,

science fair, etc.); Occupational Activities (Journalism / Yearbook club, and vocational education

clubs), Social Activities (Service clubs and Hobby groups) and Student Government. If a student

participated in one or more of the activities listed under the category, that student was coded as

“1” for that variable. For example, a female 10th grade student who played basketball, softball,

and golf, as well as being in the drama club and serving as vice-president of her class would have

a “1” for the general extracurricular participation variable, a “5” on the total extracurricular

participation variable, and a “1” for High Profile Sports, a “1” for Low Profile Sports, a “1” for

Fine Arts, and a “1” for Student Government.

The outcome measures are the student’s grades in 12th grade, and dichotomous variables

for Graduating from High School, and for attending some form of Postsecondary Education

(PSE). In the NELS, grades are measured between 1 and 13. According to the NELS electronic

codebook, “[Grades are measured so that] '01.00' represents the highest grade (comparable to

'A+') and '12.01 - 13.00' represents the lowest grade (comparable to 'F').” To analyze overall

GPA, I averaged student grades in English, Math, Science, and Social Studies. I then subtracted

that average from 13, to make the analyses of GPA more intuitive (i.e., 0 represents an “F”, and

12 represents an “A+”. Graduating from high school does not include obtaining a GED, since

labor market studies have shown that the outcomes for GED holders are more akin to dropouts

than holders of the diploma (Cameron and Heckman 1993). As Cameron and Heckman (1993)

note, “GED recipients are statistically indistinguishable from high school dropouts in terms of

42

their hourly wages and hours of work and have lower wages and hours of work than traditional

high school graduates” (p. 25).

Centering of Variables

Multilevel modeling makes centering variables extremely easy, and this technique has

several benefits for the analyst and the eventual audience for the research. Centering a variable

involves recalculating the metric of a variable so that its mean value takes on a value of 0 (zero),

and the other scores then become deviations from that “center point”. For example, the

percentage black of a neighborhood ranges from 0% (no black residents) to 100% (all black

residents. If the proportion of residents who are black in the average neighborhood was 25%,

when centering this variable any neighborhood with 25% would be given a value of 0; a

neighborhood with 35% would be given a value of 10, and a neighborhood with 15% would be

given a value of –10, and so forth.

In multilevel models, the analyst has two choices for centering – grand mean centering,

or group mean centering. Grand mean centering is essentially described above. Group mean

centering differs in that the variable is centered not around its overall mean, but instead each

grouping of cases is first grouped within the next higher level, and then each group is centered

around its group mean. An example will clarify. In this study, neighborhoods are grouped within

schools. Group mean centering a neighborhood-level variable like percentage black means that

all the neighborhoods in a school are grouped together, and the mean of their percentages of

black residents is calculated. Then each neighborhood is scored as a deviation from that mean.

Say there are 3 neighborhoods represented within a school. These neighborhoods have black

populations of 10%, 15%, and 20% respectively. The mean percentage black is 15%, so the first

neighborhood is given a score of –5 (or -.05, depending on the metric being used), the second

43

neighborhood is scored as 0, and the third is scored as 5. In another school there may also be

three neighborhoods represented, with black percentages of 15%, 20%, and 25%. For these

neighborhoods, the mean percentage is 20%, so now the neighborhood with 15% black residents

is given the score of –5, and so forth.

The value of group mean centering lies in the fact that it gives locally specific deviations

from a mean for the measures that are centered. Coming from a neighborhood that is 10% black

may have different contextual meanings, depending on whether the rest of the students in the

school one attends live in neighborhoods that have no black residents (in which case the student

may be considered as coming from the “bad” part of town) or if the other students live in areas

with very high black concentrations (in which case the student might be considered as coming

from the “good” side of town.) Because of this, the percentages themselves are of little value;

therefore centering such variables is desirable. But centering around the national (or grand) mean

is not as useful as group centering, since the local context is what matters in the daily lives of

students, not the national picture. In other words, for a student attending a school where the

average neighborhood is 75% black, coming from a neighborhood with 25% black residents

would be above the national average (the grand mean), but this student would be coming from a

“white” area as far as his schoolmates were concerned. The only limitation on group mean

centering is that there must be a level of aggregation above that which the analyst has a variable

s/he wants to group mean center. Thus, in my study, I can group mean center variables at the

Individual Level (grouped at the Neighborhood Level), and variables at the Neighborhood Level

(grouped at the School Level.) I can only grand mean center variables at the School Level, since

there is no level of aggregation above the School Level.

44

I have group mean centered SES and Test Score at the Individual Level, but not the

dummy variables for race, gender, or family structure. Also group mean centered are all of the

variables at the Neighborhood Level. Furthermore, I have grand mean centered all of the

variables at the School Level except the dummy variables for urbanicity and private schools.

These procedures provide for easier interpretations of results. For example, the intercept of the

models regressing the odds of HS graduation represents the odds of graduation for a white, male

student of average SES, from a two-parent home, with an average test score, living in a

neighborhood of average demographics (relative to his school), and attending a suburban public

school with demographics at the national averages.2

Model, Analytic Strategy and Study Hypotheses

The issue at hand is to investigate the links between neighborhood of residence,

extracurricular participation, and educational attainment. A heuristic model is pictured in Figure

3.2 to demonstrate the hypothesized effects. The bold paths are those of primary interest (i.e.,

hypothesized about) in this study, while the dashed paths are effects that are modeled as controls,

but not hypothesized.

A Three-Level Hierarchical Linear Model

To determine such effects, I estimate a three-level Hierarchical Linear Model which

predicts general extracurricular participation, total number of extracurriculars participated in, or

categorical extracurricular participation, based on student background (gender, SES, race, family

structure, and test score), neighborhood of residence, and school of attendance. These four sets of

measures (background, neighborhood, school, and extracurricular participation) are then used to

predict the outcomes of grades, high school graduation, and postsecondary education.3

45

To estimate all of the hypothesized effects, two sets of multilevel equations are required:

1) background characteristics, neighborhood, and school variables predicting extracurricular

participation; and 2) background, neighborhood variables, school variables, and extracurriculars

predicting educational outcomes. The model is specified in Table 3.2, where X1 is a vector of

student background factors (race, gender, SES, and family structure), test is the student’s score

on the NELS standardized test, xcurr is a measure of extracurricular participation (either general

extracurricular participation, total number of extracurriculars participated in, or the set of

categories of extracurricular participation), Nbhd is the vector of neighborhood variables, and

School is the vector of school variables.

46

Table 3.2: List of Equations

EXTRACURRICULAR PARTICIPATION

Level 1: (Individual Level)

XCURRijk = π0jk + π1jk(X1ijk) + π2jk(testijk) + eijk Eq. 1

Level 2: (Neighborhood Level)

π0jk = β00k + β01k (Nbhd) + r0jk Eq. 2 π1jk = β10k + β11k (Nbhd) Eq. 3

Level 3: (School Level) β00k = γ000 + γ001 (School) + u00k Eq. 4

β10k = γ100 + γ111 (School) Eq. 5

EDUCATIONAL OUTCOMES

Level 1: (Individual Level)

Yijk = π0jk + π1jk(X1ijk) + π2jk(testijk) + π3jk(xcurrijk) + eijk Eq. 6

Level 2: (Neighborhood Level)

π0jk = β00k + β01k (Nbhd) + r0jk Eq. 7

π1jk = β10k + β11k (Nbhd) Eq. 8

Level 3: (School Level) β00k = γ000 + γ011 (School) + u00k Eq. 9

β10k = γ100 + γ111 (School) Eq. 10

The exact predictors will be developed across several models. The first model will be

termed the “exogenous” model, and will include terms for individual student race, social class,

and gender, as well as the full complement of school and neighborhood variables. These

variables are exogenous to the student, in that they are essentially ascribed characteristics of each

student. Just as one does not choose one’s race, class, or gender, neither do students choose the

neighborhood they live in nor the school they attend. (Their parents, however, might make such a

47

choice, but the point is that these things are beyond the student’s control. Furthermore, these

traits are determined before the time frame of this analysis, whereas family structure and the

student’s score on the NELS standardized test are either changeable (the former) or measured as

part of the data collection (the latter), thus they are introduced in the “base” model. Introducing

these terms at this point will demonstrate if things like social class and race have their effects

through mediators like family structure and academic skill development.

The sequence ends here for the models estimating odds of extracurricular participation.

For the models estimating educational outcomes (grades, graduation, or college attendance), the

final step is to add variables for extracurricular participation as predictors. These measures are

added to the “base” model in three different ways: first, the general participation measure,

indicating whether a student was involved in any activity at all, is added to the analysis. Next, the

general measure is removed from the equation, replaced by the total participation measure, and

the model re-estimated. Finally, the total measure is removed, replaced by the set of categories of

participation, and the model is run again.

Two pairs of equations, Eqs. 3 and 5, and Eqs. 8 and 10, deserve special discussion.

These represent the inclusion of cross-level interaction terms in the analyses, where a student’s

individual race (one of the variables in the vector X1) is interacted with the various racial

percentages of the student body in either the student’s neighborhood (as in Eqs. 3 and 8) or

school (as in Eqs. 5 and 10). Each student race (i.e., black, Asian, or Hispanic) is interacted with

each of the racial population measures (percent black, percent Asian, and percent Hispanic, for

both the neighborhood and the school), producing nine interactions between the individual and

the neighborhood levels (e.g., black student * percent black in the neighborhood, black student *

percent Asian in the neighborhood, etc.) and nine interactions between the individual and school

48

levels (e.g., Asian student * percent Hispanic in the school, Asian student * percent black in the

school, etc.) After estimating the base model and each of the extracurricular models, the

interaction terms will be added and the model re-estimated. These terms show how the effect of

being of a given race varies based on the social contexts within which the individual lives and

attends school.

Hypotheses

Based on previous research, I generate and test several hypotheses, which are

summarized in Table 3.3. Two sets of hypotheses are presented, one for extracurricular

Extracurricular Participation

Racial and ethnic minority students are usually less integrated with their schools (Hanks and

Eckland 1976; Smith 2003), particularly in schools where a relatively large proportion of the

student body is white (Farkas, Lleras, and Maczuga 2002). Thus, Hypothesis 1 (H1) predicts

minorities to be less involved in the extracurriculum than whites, while H5 says this effect will be

less substantial in schools where the student is not as isolated in terms of race / ethnicity.

Alternative family structures often provide fewer resources – economic, social, emotional, time,

etc. – to support involvement in activities (Downey, 1995; Mclanahan and Sandefur 1994;

Powell and Steelman 1990). Thus students from non-biological two-parent families will also

have lower rates of participation.

Conversely, students from higher-SES backgrounds have greater resources, which

facilitates their involvement in extracurriculars, as does the achievement orientation

characteristic of parents and students from such backgrounds. Females have also been shown to

have higher rates of participation (Smith 2003). Thus H2 predicts higher rates for these groups.

49

Table 3.3: Summary of Study Hypotheses










Because of the sparse resources, and oppositional culture (Farkas, Lleras, and Maczuga 2002;

Ogbu 1990) associated with disadvantaged neighborhoods, students from such areas will in

general be less able and less inclined to be involved (H3), while minority and “broken family”

students from such neighborhoods face a kind of “double disadvantage” that further reduces

their rates of participation (H4). In other words, being a student who is of minority status, from a

non-biological family, of any SES, or female, from a disadvantaged neighborhood makes one

less likely to participate in the extracurriculum than the same type of student from a non-

disadvantaged neighborhood. A countering effect to this is hypothesized in H5, where being a

racial or ethnic minority has less of a negative effect on participation if one attends a school with

50

a larger proportion of students of the same racial / ethnic background. Because a student in such

an environment may feel less alienated from the school, participation should be more likely.

Educational Attainment

As shown in previous research, participating in the extracurriculum benefits educational

outcomes (H5, H6 and H7) (Broh 2002; Furstenberg and Hughes 1995; Hanks and Eckland 1976;

Howell, Miracle, and Rees 1984; McNeal 1995; Smith 2003). The only addendum that needs be

made to these hypotheses regards Hypothesis 7. Occupational Activities include things like

Future Farmers of America and vocational education programs, which are geared towards

immediate employment after high school. However, this category also includes Journalism /

Yearbook programs, Future Teachers of America, etc., which may lead to college attendance in

order to complete preparation for entry into such careers, though not necessarily. For these

reasons, participation in Occupational Activities is hypothesized to have negative effects on

postsecondary attendance.

SUMMARY

This research employs a multilevel analysis to evaluate the effects of student background,

neighborhood of residence, school of attendance, and extracurricular participation on educational

outcomes. Students, nested within their neighborhood and school contexts respectively, are

influenced by these contexts, and make choices to participate in certain activities, and not in

others (or any at all). The forces shaping these decisions stem from the contexts of students’

lives, and participation has subsequent effects on their educational attainment.

51

REFERENCES

Aaronson, D. 1998. "Using Sibling Data to Estimate the Impact of Neighborhoods on Children's Educational Outcomes." The Journal of Human Resources 33:915-946.

Beveridge, Andrew A. and Sophia Catsambis. 2002. "Vital Connections for Students at Risk: Family, Neighborhood and School Inlfuences on Early Dropouts." in American Educational Research Association Annual Meeting. New Orleans, LA.

—. 2003. "Adolescent at-risk behaviors: A multilevel analysis of family, neighborhood, and school factors affecting adolescent behavioral outcomes." in Annual Meeting of the American Sociological Association. Atlanta, GA.


Brooks-Gunn, Jeanne, Greg J. Duncan, and J. Lawrence Aber. 1997a. Neighborhood poverty: Vol. 1. Context and consequences for children. New York: Russell Sage Foundation.

—. 1997b. Neighborhood poverty: Vol. 2. Policy implications in studying neighborhoods. New York: Russell Sage Foundation.

Brooks-Gunn, Jeanne, Greg J. Duncan, Pamela K. Klebanov, and N. Sealand. 1993. "Do neighborhoods influence child and adolescent development?" American Journal of Sociology 99:353-395.

Cameron, S.V. and J.J. Heckman. 1993. "The Nonequivalence of High School Equivalents." Journal of Labor Economics 11:1-47.

Catsambis, Sophia and Andrew A. Beveridge. 2001. "Does Neighborhood Matter? Family, Neighborhood and School Influences on Eighth-Grade Mathematics Achievement." Sociological Focus 34:435-457.

Chase-Lansdale, P. Lindsay and R.A. Gordon. 1996. "Economic hardship and the development of five- and six-year olds: Neighborhood and regional perspectives." Child Development 67:3338-3367.

Crane, J. 1991. "The epidemic theory of ghettos and neighborhood effects on dropping out and teenage childbearing." American Journal of Sociology 96:1126-1159.

Curtin, T.R., S.J. Ingels, S. Wu, and R. Heuer. 2002. "National Education Longitudinal Study of 1988: Base-Year to Fourth Follow-up Data File User's Manual (NCES 2002-323)." U.S. Department of Education, National Center for Education Statistics., Washington, DC.

Duncan, G. J. 1994. "Families and neighbors as sources of disadvantage in the schooling decisions of White and Black adolescents." American Journal of Education 103:20-53.


Furstenberg, Frank F. Jr. and Mary Elizabeth Hughes. 1995. "Social Capital and Successful Development among At-Risk Youth." Journal of Marriage and the Family 57:580-592.


Howell, F., A. Miracle, and C.R. Rees. 1984. "Do High School Athletics Pay? The Effects of Varsity Participation on Socioeconomic Attainment." Sociology of Sport Journal 1:15-25.



52

Smith, Jason M. 2003. "Extracurricular Participation in High Poverty Schools." in Annual Meeting of the American Sociological Association. Atlanta, GA.

1 These are termed “High-Profile Sports” because, in the United States, the most popular sports, in both live spectatorship and television viewership, are these football, basketball, and baseball. Softball is included to even the balance between males and females, though there is no counterpart to football. While soccer is the most popular sport internationally, it does not hold equal status in the U.S. with these other sports. 2 Technically, the demographics of such a school would be at the averages of the overall sample in this study. But since the NELS contains a nationally representative sample of schools, this statement is acceptable. 3 Separate models will be run for each version of the extracurricular variables, and for each outcome.

53

RESULTS

Descriptive statistics for the data used in this study are contained in Table 4.1,

indicating means and (where appropriate) standard deviations for each measure. The vast

majority of students (83%) participated in some way in the extracurriculum, and averaged nearly

2 activities (1.86). Academic clubs (including things like Science Fair) had the highest rate of

participation, at 37%, while cheerleading / Pom / Drill Team was the lowest at 8%. The average

neighborhood in which students lived was 11% black, 3% Asian, and 8% Hispanic, while the

average school was 14% black, 4% Asian, and 10% Hispanic. Student grades (as measured for

this study – see Chapter 3) averaged 6.27 (or about a C+), with a standard deviation of 2.26.

Nearly all students (98%) had graduated from high school, and 85% had gone on to attend some

form of postsecondary education (be it a trade school, four year university, etc.).

While these attainment rates are high, recall from Chapter 3 that only students who had

not dropped out before the 10th grade and who did not change schools between 10th and 12th

grades were included in these analyses. Students who drop out before 10th grade or change

schools during high school are among those most likely not to graduate (see, for example,

Rumberger 1995; Rumberger and Lawson 1998; Swanson and Schneider 1999); thus,

eliminating such students from my sample produces a high school graduation rate that exceeds

the usual. The complete NELS dataset indicates that 87% of students received a high school

diploma, a percentage consistent with known graduation rates. Furthermore, the rate of

postsecondary attendance appears high as well. However, according to the National Center for

Education Statistics’ report The Condition of Education Indicator 22, “Overall, about three-

quarters of 1988 8th-graders participated in some postsecondary education by 2000.” Like the

high school graduation rate for this sample, the postsecondary attendance rate of 87% is higher

54

than expected as a result of the sample construction process. Those who drop out do not go on to

attend postsecondary forms of education, and I have eliminated most of those who drop out,

yielding the high attainment figures for this group of students.

This study focuses on two primary research questions: 1) Do neighborhood and school

contexts influence extracurricular participation; and 2) Does extracurricular participation affect

educational attainment controlling for these other influences? This chapter will review the results

of an extensive set of regression analyses designed to answer these very questions framed within

the hypotheses from the previous chapter. For the sake of review, these hypotheses are presented

once more, this time in Table 4.2, along with a second section to the table that indicates whether

or not support was found for each. The equation numbers to the right of each hypothesis refer to

the equations described in Chapter 3.

The rest of this chapter will take each of the hypotheses in the order shown in Table 4.2,

examining the results of the relevant regression models and evaluating whether support was

found. In such a way, I will cover results at the individual, neighborhood, and school levels.

First, a word is needed regarding the organization of the presentation of the results. The

actual coefficients from the linear and logistic regressions are presented in Tables 4.3 through

4.18, and any coefficients of particular interest are converted into Odds Ratios in the text. Tables

4.3 through 4.13, plus Tables 4.15 and 4.17, present three models for each outcome: 1) a model

of the exogenous factors (all the school and neighborhood variables, as well as the individual

level measures of SES, race, and gender); 2) the “Base Model”, which adds to the exogenous

factors the measures of family structure and standardized test scores; and 3) the “Interaction

Model”, which adds to the aforementioned variables the interaction terms between individual

race and the school and neighborhood race measures. Tables 4.14, 4.16 and 4.18 present the

55

results of 3 pairs of regressions for each of the three final outcome variables (Grades, High

School Graduation, and Post-Secondary Attendance): the first regression of each pair represents

the results of adding one of the three measures of extracurricular participation to the Base Model

for that outcome, while the second regression of the pair shows the results of adding interaction

terms to the previous model.

EXTRACURRICULAR PARTICIPATION

H1: Ethnic minority students and students with family structure other than two biological parents will have lower participation in all categories of extracurriculars. The first part of hypothesis 1 (H1), that minority students will have lower rates of

participation, stems from the position that minority students are less integrated into their schools,

that their peers discourage participation, and that they come from neighborhoods that discourage

participation. However, when controlling for individual and contextual factors related to

participation, minority students either have no difference from white students (in the case of

black and Hispanic students), or are more likely to participate (in the case of Asian students). I

shall now analyze in greater detail the patterns for each minority group.

Asians. In the Exogenous and Base Models, Asian students across the board are more involved

than their white peers for nearly every measure of participation. Their odds of “any”

participation are higher, as is the number of activities in which they participate. In terms of

categories of activities, Asian students are more likely than their white counterparts to be

involved in all categories except High Profile Sports (where they are less likely to take part), and

Cheerleading and Fine Arts (where there are no differences.)

Once the interaction terms are entered into the equations, there is no difference between

Asian students and white students in terms of their odds of participation in some form of

extracurricular activity, but Asian students still take part in more activities overall than whites.

56

The only categories of activities where Asians are significantly more likely than white students

to take part when controlling for the interaction between race and neighborhood and school

contexts are Academic Clubs, Occupational Activities, and Social Activities. These higher odds

support the perception of Asian students being more invested in and closely identifying with the

educational system. Indeed, the odds of an Asian student taking part in an Academic Club is at

least (depending on which model we look at) 78% greater than those for a white student, the

largest single effect for any of the race variables. Only the effect of being black on High Profile

Sport participation approaches this magnitude. Overall, Asian students are more involved in the

extracurriculum than white students, other things being equal.

Hispanics. Students of Hispanic descent have virtually no differences in their rates of

participation in the extracurriculum when compared with their white counterparts, even when

including the interaction terms. The “Hispanic” variable did not have a significant effect in any

of the regression models. This may indicate that Hispanic students have been the most successful

in assimilating themselves to mainstream practices relative to the extracurriculum, blending in

with the white students in their schools to the point that they are statistically indistinguishable

from them in terms of their participation. Although standardized test scores and graduation rates

for Hispanic students continue to show an achievement gap with whites (cf. Roscigno 2000), this

gap is apparently not due to differential participation within their schools and school activities (at

least activities outside of the classroom.)

Blacks. In general, black students are just as involved as white students in the extracurriculum.

There are two glaring exceptions to this pattern: High Profile Sports and Social Activities. As

might be expected, black students have higher odds of participation in High Profile Sports than

white students. As mentioned earlier, this effect is larger than any other race effect other than

57

that of being Asian on participation in Academic Clubs, increasing the odds of participation by at

least (again, depending on which model is examined) 82%. On the flip side, blacks participate in

Social Activities (Service clubs and Hobby groups) at lower rates than whites, and much lower

rates than Asians (who are significantly more likely to participate than whites.) Overall,

however, black students have the same odds of participating in some form in the extracurriculum

as whites, and in just as many activities (or slightly more, according to the Base Model).

Overall, the first part of H1, that minority students will have lower rates of participation

than whites, is not supported. Asian students tend to be more involved in general than whites,

while Hispanics and blacks are just as involved, and in some cases more so, controlling for

relevant factors. Some differences arise between minority students and whites when I interact

their race with their school and neighborhood racial compositions, but those results will be

discussed later in this chapter.

The second part of H1, that students from families with a structure other than two

biological parents (or a parent and stepparent) will have lower rates of participation, receives

more support. Somewhat surprisingly, single-parent family structure have fewer significant

effects than “Other” (other relative or non-relative) family forms, and these effects are smaller in

magnitude.

In terms of participating in at least one activity (general participation), single-parent

homes had a negative effect, but one that only reached the p <.10 level of significance and that

had a smaller magnitude than the negative effect of other family forms, which was significant at

the p <.05 level. Both alternative family forms had detrimental effects on rates of participation,

decreasing the odds of some form of participation approximately 15-20%. Single parent families

had no significant effect on the number of activities in which a student participated, but other

58

family forms decreased the number of activities. Specific categories that saw lower rates of

participation (p <.10) from those in single parent homes include High Profile Sports (in the

Interaction Model), Fine Arts activities, and Academic Clubs, while those from other family

forms were less likely to take part in both High and Low Profile Sports, and Fine Arts. The only

effect that was positive and at least marginally significant (p <.10) was a student from a single

parent family participating in Social Activities, which increased a student’s odds of participation

about 17%.

Overall, non-traditional family forms (i.e., families other than those with two

parents – either biological or step) had a negative effect on extracurricular participation. These

results are consistent with H1.

H2: Students of higher SES and Females will have higher participation in all measures of extracurriculars. Socioeconomic Status (SES) has historically been one of the strongest predictors of

virtually all outcomes in social research, and this study is no different. In every model except

those for Occupational Activities, SES is significant at the highest level (p <.001), and one of, if

not the, largest effect in magnitude. In all models, the effect of having a higher SES background

on the odds of participation in the extracurriculum is positive.

The only other factor that compares with this effect is Female. Girls, across the board,

participate in more activities overall, and are more likely to take part in all of the categories, save

two: either High or Low Profile Sports. Like SES, the Female effect is also consistently

significant at the highest level, and is among the largest in magnitude. The only model in which

Female is not significant is for general participation –participation in at least one activity. That

girls take part in more activities, are more likely to engage in 6 of the 8 categories of specific

activities, but are no more likely to take part in the extracurriculum in general, may seem

59

inconsistent. However, this is easily understood when one considers the much greater

participation of boys in sports. The overwhelming predominance of boys in sports – both High

and Low Profile, evidenced by the large, negative coefficient for Female – offsets the greater

odds of girls taking part in all of the other categories, thus rendering the Female effect non-

significant in the general participation model.

Based on these results, Hypothesis 2 is overwhelmingly supported. H3: Neighborhood disadvantage will lead to lower rates of participation in all measures of extracurriculars. Neighborhood variables had surprisingly little explanatory power in my analyses, with

only a few significant coefficients across the various models. Neighborhood context made no

difference in general participation rates, though increasing economic distress (the combination of

unemployment rate and proportion of very poor families) in the neighborhood decreases the

number of activities in which a student takes part. Immigrant neighborhoods (those with high

proportions of foreign born residents) decrease the likelihood of participation in High Profile

Sports, but no neighborhood factor made a difference in Low Profile Sport participation. Those

from neighborhoods with higher than average (for their school) proportions of foreign-born

residents had much lower odds of cheerleading, and those from neighborhoods in greater

economic distress also were less likely to take part in this activity as well as to participate in

drama or music groups (Fine Arts activities). One positive neighborhood effect on being

involved in Fine Arts activities was found: living in a neighborhood with larger proportions of

black residents increased participation in drama, music, and the like. Academic club participation

was unaffected by neighborhood context, but neighborhoods with higher proportions of female-

headed households greatly diminished the odds of being involved in Student Government.

Countering this effect is the result that “blacker” neighborhoods increased the odds of

60

participating in Student Government. These odds were reduced to nearly zero when living in a

neighborhood dominated by female-headed households, but neighborhoods that were blacker

than average (for the school) raised those odds between 400 and 600%. With the high rate of

single-parenthood among African Americans, this result shows that it is not the race of the

neighborhood, but the structure of families that is the important consideration. A similar result

occurs when predicting the odds of taking part in Occupational Activities. Neighborhoods with

high rates of female-headed households again lower odds of participation in this category to

nearly zero, while blackness of the neighborhood raises participation odds 250-300%. Like Low

Profile Sports and Academic Clubs, Social Activities were unaffected by neighborhood

variables.

In sum, neighborhood race measures had little impact on extracurricular participation, but

measures of the overall social class of the neighborhood did have significant effects in half of the

models. In three of the five instances (total participation, cheerleading / pom / drill team, fine

arts, student government, and occupational activities) where a social class variable made a

difference (either proportion of female-headed households or economic distress), blackness of

the neighborhood worked in the opposite direction (the latter three instances).

The next two hypotheses involve interaction effects. When discussing interaction effects,

it is important to be clear in their interpretation. The reference category for race is white students,

so any interpretation of an interaction effect is in reference to that group. For example, a

significant and negative interaction effect on participating in the extracurriculum between

“black” on the individual level, and the proportion of black residents in a neighborhood suggests

that a black student in that neighborhood is significantly less likely to participate than a white

student in that neighborhood. A significant and positive interaction effect on HS graduation

61

between “Asian” and the percentage of Hispanic students in a school indicates that an Asian

student is significantly more likely to graduate than a white student in that school.

H4: Neighborhood race will make more positive the effect of the corresponding individual level race, and will make more negative the effect of different individual race (interaction effects).

This hypothesis is based on the idea that living in a neighborhood with others of the same

race will improve feelings of attachment and provide higher levels of social support, thereby

encouraging participation in activities in the school they attend. Living in a neighborhood

dominated by races different from the race of the student is thought to heighten feelings of

estrangement or alienation from the community and its associated institutions; therefore

participation would be less likely.

There is little evidence of any neighborhood racial context altering the main effects of the

race variables, as nearly all of the interaction terms are non-significant. Out of 180 interaction

terms (18 per regression, across 10 outcomes), only 17 are significant at the p <.05 level (and

only an additional 3 when I relax the significance criterion to the p <.10 level). These significant

effects are shown in Table 4.19. The shaded areas of Table 4.19 represent where positive effects

were predicted by Hypothesis 4, but only one of the significant interaction effects was positive,

and it was not one that was predicted to be so. Hispanic neighborhoods had the most significant

effects, while Asian neighborhoods had the strongest. Black neighborhoods only had 3

significant effects, including the sole positive coefficient. The negative effects of black

neighborhoods were also the smallest of any of the significant coefficients produced.

For both Hispanics and blacks, living in an Hispanic neighborhood lowers the odds of

participating in the extracurriculum in general, and of taking part in either cheerleading or

occupational activities. I have already shown that blacks and Hispanics have little or no

62

differences with white students in terms of their odds of participation, so the negative

interactions indicate that Hispanics or black living in an Hispanic neighborhood are less likely to

participate compared to the base category – white students. For both blacks and Asians, hailing

from an Hispanic neighborhood lessens the odds of participating in Fine Arts activities, and

black students take part in far fewer activities overall (nearly 4 fewer, on average) when residing

in an Hispanic neighborhood. Clearly, black students’ participation in extracurriculars suffers the

most from living in an Hispanic neighborhood (compared to white students), but contrary to

Hypothesis 4, Hispanic students’ participation suffers as well.

These results suggest that living in a neighborhood with greater proportions of minorities

– regardless of which minority group it is, and regardless of the student’s race – is detrimental to

the involvement a student has in school activities, in terms of lowered odds of participation in the

extracurriculum compared with white students in those neighborhoods.

H5: School race variables will make more positive the effect of the corresponding individual level race, and will make more negative the effect of different individual race (interaction effects).

Based on similar ideas as the previous hypothesis, H5 suggests that, for example, being

black in a mostly black school would serve to increase the rate of participation in extracurriculars

for black students, while being black in an Hispanic school would serve to decrease it, all relative

to white students in such a school. As with the neighborhood level interaction terms, few of the

effects were significant. Of the 180 interaction effects, 15 coefficients were significant at the p

<.05 level, with 2 more at the p <.10 level, and again, black students had the most effects. (These

significant effects are shown in Table 4.20.) While the significant effects were spread between

the three racial categories of neighborhood, most of the significant school effects were for Asian

schools (12 of the 17), whereas black schools only had two significant effects, and Hispanic

63

schools three.

Interestingly, Hispanic students experienced no significant effects from either Hispanic or

black schools, and black students only saw one effect from a non-Asian minority school, in

which “blacker” schools decreased participation in Occupational Activities for black students

compared with whites. Asian students in schools with higher levels of African American

enrollment were less likely to participate in the extracurriculum in general, and in schools with

higher levels of Hispanic enrollment they were less likely to take part in Fine Arts, but more

likely to be involved in both Occupational and Social Activities.

The Asian schools had a much broader set of effects on students of all races. Asian

students in Asian schools were the only group to realize an effect consistent with H5, in that their

odds of participation in Cheerleading were increased, as were these same odds for Hispanic

students (black students were unaffected.) For all three categories of student race, when

compared with white students, being in an Asian school lessened the odds of participating in

Low Profile Sports, probably a result of the fact that Asian-dominated schools are in urban, high

immigrant, and hence lower-SES, areas, with fewer resources for such “country club” activities

as swimming, golf, or tennis. Both Hispanic and black students in Asian schools were less likely

to take part in Academic Clubs, perhaps reflecting the academic dominance (both perceived and

real) associated with Asian students. Hispanic students in Asian schools also were less likely to

take part in Occupational activities than whites, while black students in these schools had

lowered odds of participation relative to whites in Fine Arts and Social Activities, and

participated in fewer activities in general.

In sum, Asian schools had the greatest impact on student participation, particularly

among black students, who were far less likely to participate in half of the categories of

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activities, participated in fewer total activities, and had much smaller odds of participating at all

(compared with whites) if they attended an Asian school.

EDUCATIONAL ATTAINMENT

The final three hypotheses – H6, H7, and H8 – deal with educational achievement and

attainment, measured by the student’s average grades in the 12th grade, the odds of graduating

from high school, and the odds of attending some form of postsecondary educational institution

(PSE). Previous research, as reviewed in Chapter 2, suggests that participating in the

extracurriculum has beneficial effects on all of these. My hypotheses reflect this, with the added

nuance that Occupational Activities – participating in clubs that are geared largely towards

employment after high school, rather than college – will decrease the odds of PSE. Participation

in the extracurriculum improves integration into the school community, surrounds students with

achievement-oriented peers and adult role models, and develops skills that are generalizable to

school success, all of which improve the odds of graduating high school and PSE. I examine the

results related to H6 through H8 in turn.

H6: Extracurricular participation will increase 12th grade GPA.

Involvement in extracurricular activities had consistently positive effects on grade point

average, regardless of the mode of operationalization. General participation, (i.e., being involved

in at least one activity) raised GPA by .476, which is approximately ¼ of a letter grade on the

NELS-defined scale of 1-13 (see Chapter 3 for more details.) This effect remained nearly the

same (.468) even when incorporating into the model the interactions between individual and

contextual level race variables. Each additional activity one participates in raises GPA by .219.

For the particular categories of activities, the strongest effect was for Academic Clubs (.548)

followed closely by Student Government (.408). Low Profile Sports had a stronger effect than

65

High Profile Sports, though both were clearly positive and significant (.281 and .110,

respectively), and Social Activities also was associated with an increase in GPA (b=.199).

Cheerleading, Fine Arts, and Occupational Clubs had no significant effects.

These results support H6, that participation in the extracurriculum increases student GPA

in 12th grade, independent of student race, social class, gender, family structure and standardized

test score, and independent of the school or neighborhood context in which the student lives.


Regardless of how extracurricular participation was included in the model –general

participation, total activities, or the individual categories of activities – extracurricular

participation had a statistically significant, positive effect on the odds of graduation, both in the

straight outcome model and in the outcome model with interaction effects. For the model without

interactions, those who participate in at least something have double the odds of graduating

(odds ratio=1.97) compared with non-participants, and for every additional program of

involvement, the odds increase another 40%. In the interaction model, these effects remained

nearly unchanged, with odds ratios of 1.85 and 1.41, respectively.

Several individual categories had strong, positive effects, in both the outcome and

interaction models, on the chances a student would graduate as well. High Profile Sports

improved the odds by 62.5% (56% in the interaction model), while Low Profile Sports

participants had an odds ratio of 1.86 over non-participants (1.69 in the interaction model).

Cheerleaders actually had the highest odds ratio over non-participants, 2.58, followed closely by

those involved in Service or Hobby Clubs (the Social Activities category) at 2.33 (2.59 and 2.03,

respectively, in the interaction model). Academic Clubs also had a significant effect, improving

the odds of graduation by 58% (48% in the interaction model). Fine Arts participation improved

66

the odds of graduation as well, but only when controlling for the interaction between individual

race and neighborhood racial composition, increasing the odds of graduation by 22%. Only

Occupational Clubs and Student Government failed to have a significant effect on the odds of

graduation.1

Hypothesis 6 is overwhelmingly supported by these results. Participation in virtually any

activity outside the normal curriculum of a school improves the odds of graduating.


The final hypothesis of this study centers on attending some form of educational program

or institution after high school, or “Postsecondary Education” (PSE). Once again, nearly all of

the extracurricular measures were found to have significant, positive effects on the outcome. The

only category of extracurricular participation not to have such an effect was Occupational

Activities, which had a negative effect, as hypothesized. This effect approached significance at

the p <.10 level, with p=.114 in the straight outcome model, and .105 in the interaction model.

By convention, it can be concluded that the second part of H7 is not supported, since the effect

was not significant at the usual p<.05 level, or even .10. However the results are in the

anticipated direction, and they approach significance. I leave it to the reader to decide whether

the results support that aspect of H7, while the first half of the hypothesis is clearly supported.

Odds ratios of attending PSE for participants over non-participants range from 1.36 (for

cheerleading, in the interaction model) to 1.98 (for Student Government participants, in the

straight outcome model). In other words, participation in any part of the extracurriculum

improves the odds of PSE attendance between 36% and 98%, nearly doubling the chance a

student will further their education beyond high school when controlling for other relevant

factors. Given these findings, Hypothesis 7 is strongly supported.

67

SUMMARY AND REVIEW OF RESULTS

The results of this study are summarized in the second part of Table 4.2, showing each

hypothesis and the general findings related to it. Overall, the hypotheses of this study were

supported, though not always as strongly as expected. Among those hypotheses that were not

supported, perhaps most surprising is that ethnic minorities are actually more likely to participate

than whites, or show no significant differences (controlling for other potential covariates), and

neither neighborhood nor school racial composition makes more positive the effects of the

corresponding race variables. These last two findings are counter to Hypotheses 4 and 5, but

when one considers that individual race variables had the opposite effect from what was

hypothesized, the results may be seen as less contradictory. The interaction was expected to

make minority individuals more likely to participate in extracurriculars if they lived (or attended

school) among others of similar racial background than if they lived in an area (or attended a

school) of average racial composition. But this would mean that the greater odds of participation

shown by minority students would be greater yet; thus it is understandable that the interaction

would prove non-significant.

Instead, the interactions suggest that living in an area of greater minority concentration –

regardless of which minority racial group it is – is detrimental to the odds of participation.

Schools had somewhat more mixed effects. Attending a school with greater levels of Asian

students generally lessens the odds of participation for both Hispanic and black students when

compared with whites, but Hispanic and black schools have little effect on participation.

Discussion of these trends and their implications is made in the following chapter.

68

TABLES:

For all tables, +p<.10; *p<.05; **p<.01; ***p<.001

Table 4.1 – Descriptive Statistics Level 1 – Individuals N=8346 Level 2 – Neighborhoods N=2457

VARIABLE MEAN SD VARIABLE MEAN SD Any Participation? 0.83 % Black 0.11 0.20Total Participation 1.86 1.39 % Asian 0.03 0.06Hi Profile Sports 0.30 % Hispanic 0.08 0.17Low Profile Sports 0.34 % Foreign Born 0.08 0.11Cheerleading / Pom 0.08 % Female Households 0.11 0.07Fine Arts 0.29 Unemployment Rate 0.07 0.04Academic Clubs 0.37 % w/ Very Low Income 0.09 0.08Student Government 0.09 Economic Distress 0.02 1.04Occupational Clubs 0.21 Social Activities 0.19 Level 3 – Schools N=1116 SES 0.02 0.78 Urban School 0.34 Asian 0.07 Rural School 0.28 Hispanic 0.11 Private School 0.17 Black 0.09 Total Enrollment 1183.10 742.63Female 0.53 % Asian 0.04 0.08Single Parent Family 0.14 % Hispanic 0.10 0.20Other Family Form 0.11 % Black 0.14 0.22Test Score 52.31 9.61 % Single Parent 0.02 0.01Grades in 12th Grade 6.27 2.26 % LEP 0.01 0.01HS Graduate 0.98 % on Free Lunch 0.19 0.21Attend Some PSE 0.85

69

Table 4.2: Summary of Study Hypotheses and Support / Non-Support for Each










H1: Ethnic minorities lower participation NOT SUPPORTED H2: Higher SES higher participation SUPPORTED H3: Contextual Disadvantage lower participation SUPPORTED H4: Minority*Minority Neighborhood lower participation SUPPORTED H5: Minority*Minority School lower participation PARTLY SUPPORTED H6: Extracurriculars increased grades in 12th grade SUPPORTED H7: Extracurriculars increased odds of HS Graduation SUPPORTED H8: Extracurriculars increased odds of PSE SUPPORTED Occupational Activities decreased odds of PSE NOT SUPPORTED

70

Table 4.3 – Regression Coefficients for Odds of Any Extracurricular Participation INDEP VAR’S Exo Base Interacts L Level 3 intercept 1.523 1.593 1.615 E Urban School -0.085 -0.084 -0.036 V Rural School 0.057 0.061 0.065 E Private School 0.378 * 0.344 * 0.335 * L Total Enrollment 0.000 *** 0.000 *** 0.000 *** % Asian -0.685 + -0.657 + 0.858 3 % Hispanic 0.334 0.259 0.402 % Black -0.453 * -0.543 * -0.691 * V % Single Parent -15.698 ** -14.947 * -15.703 ** A % LEP -3.893 -3.491 -3.109 R % on Free Lunch -0.190 -0.193 -0.150

L % Black 1.035 0.969 1.458 + E % Asian -1.249 -1.161 -2.497 V % Hispanic 0.432 0.435 2.649 + E % Foreign Born -0.525 -0.526 -0.509 L % Female Households -1.756 -1.659 -1.328 2 Economic Distress -0.152 -0.157 -0.150 SES 0.587 *** 0.414 *** 0.427 *** RACE Asian 0.355 ** 0.287 * 0.175 * % Asian in School -1.727 * % Hisp in School -0.199 L * % Black in School -1.554 * E * % Black in Nbhd -1.423 V * % Asian in Nbhd 3.675 E * % Hisp in Nbhd -0.831 L Hispanic -0.121 -0.067 -0.024 * % Asian in School -2.135 * % Hisp in School -0.429 * % Black in School -0.808 * % Black in Nbhd -1.651 1 * % Asian in Nbhd -5.327 * % Hisp in Nbhd -3.770 ** Black 0.104 0.225 + 0.001 * % Asian in School -9.018 ** * % Hisp in School -0.147 V * % Black in School 0.537 A * % Black in Nbhd -1.693 * R * % Asian in Nbhd 5.486 S * % Hisp in Nbhd -6.852 *** FEMALE 0.108 + 0.084 0.084 FAMILY STRUCTURE Single Parent Family -0.146 + -0.157 + Other Family Form -0.231 * -0.236 * Test Scores 0.038 *** 0.038 ***

71

Table 4. 4 – Regression Coefficients for Total Extracurricular Participation INDEP VAR’S Exo Base Interacts L Level 3 intercept 1.584 1.610 1.623 E Urban School -0.024 -0.021 -0.010 V Rural School 0.084 0.086 0.085 E Private School 0.342 *** 0.331 *** 0.319 *** L Total Enrollment 0.000 *** 0.000 *** 0.000 *** % Asian -0.278 -0.247 0.465 3 % Hispanic -0.105 -0.150 -0.212 % Black -0.186 -0.240 + -0.193 V % Single Parent -12.712 *** -12.318 ** -12.217 ** A % LEP -1.370 -1.166 -1.004 R % on Free Lunch -0.017 -0.020 -0.016

L % Black 0.425 0.388 0.522 E % Asian -0.555 -0.554 1.084 V % Hispanic 0.336 0.337 1.059 E % Foreign Born -0.083 -0.069 -0.249 L % Female Households -0.692 -0.676 -0.469 2 Economic Distress -0.114 * -0.114 * -0.108 + SES 0.461 *** 0.346 *** 0.349 *** RACE Asian 0.383 *** 0.338 *** 0.303 *** * % Asian in School -0.749 * % Hisp in School -0.175 L * % Black in School -0.415 E * % Black in Nbhd -0.944 V * % Asian in Nbhd -1.652 E * % Hisp in Nbhd -0.705 L Hispanic 0.024 0.065 0.032 * % Asian in School -1.065 * % Hisp in School 0.081 * % Black in School -0.323 * % Black in Nbhd -0.794 1 * % Asian in Nbhd -3.740 + * % Hisp in Nbhd -1.028 Black 0.055 0.137 * 0.095 * % Asian in School -4.382 *** * % Hisp in School 0.000 V * % Black in School -0.125 A * % Black in Nbhd -0.644 R * % Asian in Nbhd -4.270 S * % Hisp in Nbhd -3.746 *** FEMALE 0.284 *** 0.267 *** 0.266 *** FAMILY STRUCTURE Single Parent Family -0.075 -0.077 Other Family Form -0.151 ** -0.152 ** Test Scores 0.024 *** 0.024 ***

72

Table 4.5 – Regression Coefficients for Odds of High Profile Sport Participation INDEP VAR’S Exo Base Interacts L Level 3 intercept -0.445 -0.406 -0.424 E Urban School -0.183 * -0.183 * -0.199 * V Rural School -0.034 -0.031 -0.028 E Private School 0.043 0.034 0.045 L Total Enrollment 0.000 *** 0.000 *** 0.000 *** % Asian -0.075 -0.090 -0.075 3 % Hispanic 0.047 0.046 0.107 % Black -0.982 *** -0.969 *** -1.240 *** V % Single Parent -11.446 * -10.831 * -11.020 * A % LEP -2.088 -1.884 -2.257 R % on Free Lunch 0.155 0.159 0.176

L % Black -0.442 -0.431 -0.538 E % Asian -1.627 -1.652 2.213 V % Hispanic 0.890 0.953 0.840 E % Foreign Born -3.191 * -3.217 * -3.779 ** L % Female Households 1.204 1.247 1.393 2 Economic Distress 0.003 0.003 0.012 SES 0.187 *** 0.189 *** 0.193 *** RACE Asian -0.200 + -0.211 + -0.159 * % Asian in School 0.146 * % Hisp in School -1.048 L * % Black in School 1.209 E * % Black in Nbhd -2.215 V * % Asian in Nbhd -5.460 + E * % Hisp in Nbhd 1.324 L Hispanic 0.160 0.153 0.178 * % Asian in School -0.226 * % Hisp in School 0.056 * % Black in School 1.044 * % Black in Nbhd 0.209 1 * % Asian in Nbhd -4.629 * % Hisp in Nbhd 1.025 Black 0.602 *** 0.620 *** 0.668 *** * % Asian in School -0.551 * % Hisp in School 0.666 V * % Black in School 0.223 A * % Black in Nbhd 0.239 R * % Asian in Nbhd -7.780 S * % Hisp in Nbhd -3.127 FEMALE -1.183 *** -1.183 *** -1.185 *** FAMILY STRUCTURE Single Parent Family -0.133 -0.139 + Other Family Form -0.174 + -0.174 + Test Scores -0.004 -0.004

73

Table 4.6 – Regression Coefficients for Odds of Low Profile Sport Participation INDEP VAR’S Exo Base Interacts L Level 3 intercept -0.667 -0.626 -0.597 E Urban School -0.021 -0.021 0.010 V Rural School -0.168 * -0.165 * -0.155 + E Private School 0.419 *** 0.406 *** 0.361 ** L Total Enrollment 0.000 *** 0.000 *** 0.000 *** % Asian -0.175 -0.170 2.002 + 3 % Hispanic -0.131 -0.158 -0.127 % Black -0.686 ** -0.705 ** -0.735 + V % Single Parent -12.361 * -11.808 * -12.215 * A % LEP 6.492 * 6.758 * 5.868 * R % on Free Lunch -0.877 *** -0.882 *** -0.833 ***

L % Black 0.304 0.298 0.705 E % Asian 0.034 0.038 1.430 V % Hispanic 0.380 0.419 1.440 E % Foreign Born -1.002 -1.016 -1.626 L % Female Households 0.075 0.098 0.337 2 Economic Distress -0.138 -0.139 -0.137 SES 0.513 *** 0.451 *** 0.452 *** RACE Asian 0.198 * 0.166 + 0.164 * % Asian in School -2.929 * * % Hisp in School 0.395 L * % Black in School -0.389 E * % Black in Nbhd -1.054 V * % Asian in Nbhd -1.176 E * % Hisp in Nbhd 0.352 L Hispanic -0.118 -0.098 -0.061 * % Asian in School -2.627 + * % Hisp in School -0.314 * % Black in School -0.379 * % Black in Nbhd -1.343 1 * % Asian in Nbhd -5.407 * % Hisp in Nbhd -1.954 Black -0.074 -0.024 -0.108 * % Asian in School -8.870 *** * % Hisp in School 0.289 V * % Black in School -0.078 A * % Black in Nbhd -1.507 + R * % Asian in Nbhd -1.653 S * % Hisp in Nbhd -4.334 FEMALE -0.203 *** -0.213 *** -0.216 *** FAMILY STRUCTURE Single Parent Family -0.078 -0.082 Other Family Form -0.261 ** -0.265 ** Test Scores 0.012 *** 0.012 ***

74

Table 4.7 – Regression Coefficients for Odds of Cheerleading / Pom Team Participation INDEP VAR’S Exo Base Interacts L Level 3 intercept -4.409 -4.413 -4.454 E Urban School -0.068 -0.068 -0.053 V Rural School 0.015 0.014 -0.006 E Private School -0.193 -0.189 -0.171 L Total Enrollment 0.000 ** 0.000 ** 0.000 ** % Asian -0.446 -0.435 -3.096 + 3 % Hispanic 0.690 + 0.681 + 0.813 % Black 0.383 0.366 0.291 V % Single Parent 9.728 9.705 10.471 A % LEP -15.608 * -15.663 * -15.433 * R % on Free Lunch -0.049 -0.052 -0.087

L % Black 0.064 0.060 -0.460 E % Asian 2.163 2.110 6.469 V % Hispanic 1.241 1.197 3.593 * E % Foreign Born -4.675 * -4.615 * -4.959 * L % Female Households 1.288 1.280 1.234 2 Economic Distress -0.234 * -0.233 * -0.220 + SES 0.412 *** 0.382 *** 0.376 *** RACE Asian -0.351 -0.360 -0.279 * % Asian in School 3.522 * * % Hisp in School -1.402 L * % Black in School 0.849 E * % Black in Nbhd 6.663 *** V * % Asian in Nbhd -11.812 ** E * % Hisp in Nbhd 3.489 L Hispanic -0.005 0.008 -0.051 * % Asian in School 4.467 * * % Hisp in School -0.182 * % Black in School -1.728 * % Black in Nbhd 0.046 1 * % Asian in Nbhd -3.295 * % Hisp in Nbhd -4.493 * Black -0.176 -0.161 -0.194 * % Asian in School 1.679 * % Hisp in School 0.708 V * % Black in School 0.346 A * % Black in Nbhd 0.209 R * % Asian in Nbhd -5.064 S * % Hisp in Nbhd -6.456 ** FEMALE 2.637 *** 2.632 *** 2.632 *** FAMILY STRUCTURE Single Parent Family 0.003 0.012 Other Family Form 0.017 0.018 Test Scores 0.007 0.008

75

Table 4.8 – Regression Coefficients for Odds of Fine Arts Participation INDEP VAR’S Exo Base Interacts L Level 3 intercept -1.443 -1.399 -1.398 E Urban School 0.074 0.079 0.098 V Rural School 0.063 0.069 0.051 E Private School -0.055 -0.067 -0.075 L Total Enrollment 0.000 *** 0.000 *** 0.000 *** % Asian -0.226 -0.205 -0.972 3 % Hispanic -0.275 -0.334 -0.266 % Black -0.002 -0.057 -0.276 V % Single Parent -7.749 -6.968 -7.213 A % LEP 0.004 0.389 2.938 R % on Free Lunch 0.355 0.361 + 0.318

L % Black 0.941 + 0.927 + 1.382 * E % Asian 0.047 0.016 4.614 V % Hispanic -0.637 -0.627 0.521 E % Foreign Born 2.112 2.167 1.991 L % Female Households -0.250 -0.206 0.109 2 Economic Distress -0.253 ** -0.256 ** -0.260 ** SES 0.379 *** 0.238 *** 0.242 *** RACE Asian 0.171 0.113 -0.083 * % Asian in School 1.448 * % Hisp in School -2.045 ** L * % Black in School -1.532 E * % Black in Nbhd -2.394 * V * % Asian in Nbhd -8.167 * E * % Hisp in Nbhd -4.285 * L Hispanic -0.084 -0.039 -0.139 * % Asian in School -0.591 * % Hisp in School 0.196 * % Black in School 0.202 * % Black in Nbhd -1.000 1 * % Asian in Nbhd -9.346 * * % Hisp in Nbhd -1.126 Black 0.145 0.256 * -0.068 * % Asian in School -7.556 ** * % Hisp in School -1.311 V * % Black in School 0.521 A * % Black in Nbhd -1.061 R * % Asian in Nbhd 7.407 S * % Hisp in Nbhd -6.532 * FEMALE 0.720 *** 0.705 *** 0.701 *** FAMILY STRUCTURE Single Parent Family -0.174 * -0.187 * Other Family Form -0.298 *** -0.309 *** Test Scores 0.028 *** 0.028 ***

76

Table 4.9 – Regression Coefficients for Odds of Academic Club Participation INDEP VAR’S Exo Base Interacts L Level 3 intercept -0.913 -0.909 -0.909 E Urban School 0.063 0.075 0.105 V Rural School 0.097 0.099 0.093 E Private School 0.091 0.091 0.088 L Total Enrollment 0.000 * 0.000 * 0.000 + % Asian -0.833 * -0.782 * -0.058 3 % Hispanic -0.084 -0.152 -0.345 % Black -0.082 -0.180 -0.166 V % Single Parent -9.156 + -8.865 -8.810 A % LEP -1.810 -1.541 -1.292 R % on Free Lunch -0.065 -0.056 -0.084

L % Black 0.224 0.184 -0.004 E % Asian -1.540 -1.602 -1.747 V % Hispanic 0.596 0.591 0.682 E % Foreign Born 0.716 0.744 0.773 L % Female Households -0.726 -0.717 -0.511 2 Economic Distress -0.017 -0.014 -0.002 SES 0.429 *** 0.224 *** 0.229 *** RACE Asian 0.711 *** 0.652 *** 0.580 *** * % Asian in School -0.480 * % Hisp in School 0.237 L * % Black in School -0.297 E * % Black in Nbhd 0.741 V * % Asian in Nbhd 1.310 E * % Hisp in Nbhd 0.584 L Hispanic 0.036 0.111 0.087 * % Asian in School -2.556 * * % Hisp in School 0.143 * % Black in School -0.686 * % Black in Nbhd 0.046 1 * % Asian in Nbhd -2.680 * % Hisp in Nbhd -0.285 Black -0.156 -0.003 -0.189 * % Asian in School -8.465 ** * % Hisp in School -0.595 V * % Black in School -0.056 A * % Black in Nbhd -0.054 R * % Asian in Nbhd 0.250 S * % Hisp in Nbhd -3.053 FEMALE 0.445 *** 0.425 *** 0.428 *** FAMILY STRUCTURE Single Parent Family -0.176 * -0.182 * Other Family Form -0.053 -0.050 Test Scores 0.046 *** 0.046 ***

77

Table 4.10 – Regression Coefficients for Odds of Student Government Participation INDEP VAR’S Exo Base Interacts L Level 3 intercept -2.786 -2.874 -2.835 E Urban School -0.056 -0.032 -0.011 V Rural School -0.188 + -0.192 + -0.189 + E Private School 0.386 * 0.430 ** 0.395 ** L Total Enrollment 0.000 *** 0.000 *** 0.000 *** % Asian -0.167 -0.063 1.207 3 % Hispanic -0.216 -0.271 -0.454 % Black 0.378 0.286 0.583 + V % Single Parent -5.982 -6.444 -5.885 A % LEP 4.247 4.208 5.136 R % on Free Lunch -0.241 -0.251 -0.255

L % Black 1.400 * 1.428 * 1.776 * E % Asian 1.609 1.498 -2.784 V % Hispanic 1.588 1.552 1.626 E % Foreign Born -0.027 0.034 0.743 L % Female Households -5.325 * -5.598 * -5.724 ** 2 Economic Distress -0.125 -0.116 -0.104 SES 0.746 *** 0.482 *** 0.486 *** RACE Asian 0.388 * 0.315 * 0.272 * % Asian in School -1.828 * % Hisp in School -0.193 L * % Black in School -0.672 E * % Black in Nbhd 0.077 V * % Asian in Nbhd 7.169 E * % Hisp in Nbhd -3.036 L Hispanic 0.105 0.206 0.068 * % Asian in School -2.514 * % Hisp in School 0.111 * % Black in School -1.809 * % Black in Nbhd -3.539 1 * % Asian in Nbhd 5.568 * % Hisp in Nbhd -0.752 Black 0.021 0.187 0.138 * % Asian in School -5.688 * % Hisp in School -0.175 V * % Black in School -0.552 A * % Black in Nbhd -1.041 R * % Asian in Nbhd -5.104 S * % Hisp in Nbhd 0.839 FEMALE 0.550 *** 0.523 *** 0.519 *** FAMILY STRUCTURE Single Parent Family -0.075 -0.074 Other Family Form 0.047 0.046 Test Scores 0.063 *** 0.063 ***

78

Table 4.11 – Regression Coefficients for Odds of Occupational Club Participation INDEP VAR’S Exo Base Interacts L Level 3 intercept -1.903 -1.897 -1.872 E Urban School -0.013 -0.012 -0.011 V Rural School 0.503 *** 0.504 *** 0.492 *** E Private School 0.166 0.165 0.132 L Total Enrollment 0.000 *** 0.000 *** 0.000 *** % Asian -1.011 * -1.007 * 0.191 3 % Hispanic -0.019 -0.020 -0.547 % Black 0.465 + 0.465 * 0.823 ** V % Single Parent -22.25 *** -22.128 *** -21.943 *** A % LEP -10.05 * -9.967 * -10.244 ** R % on Free Lunch 0.578 * 0.581 * 0.570 *

L % Black 1.030 + 1.031 + 0.884 E % Asian 0.081 0.079 1.037 V % Hispanic 0.304 0.315 2.414 + E % Foreign Born 0.865 0.854 0.664 L % Female Households -2.944 * -2.929 * -3.044 * 2 Economic Distress 0.066 0.066 0.076 SES 0.054 0.046 0.042 RACE Asian 0.398 *** 0.394 *** 0.317 * * % Asian in School -1.404 * % Hisp in School 1.031 + L * % Black in School -1.288 E * % Black in Nbhd -1.277 V * % Asian in Nbhd 0.930 E * % Hisp in Nbhd -2.076 L Hispanic -0.021 -0.022 -0.054 * % Asian in School -4.066 * * % Hisp in School 0.667 * % Black in School -0.037 * % Black in Nbhd 0.596 1 * % Asian in Nbhd -7.576 * * % Hisp in Nbhd -3.721 * Black 0.032 0.043 0.191 * % Asian in School -2.399 * % Hisp in School 0.877 V * % Black in School -0.874 * A * % Black in Nbhd -0.121 R * % Asian in Nbhd -15.958 * S * % Hisp in Nbhd -6.377 * FEMALE 0.398 *** 0.398 *** 0.396 *** FAMILY STRUCTURE Single Parent Family -0.048 -0.048 Other Family Form 0.003 -0.002 Test Scores 0.001 0.001

79

Table 4.12 – Regression Coefficients for Odds of Social Activity Participation INDEP VAR’S Exo Base Interacts L Level 3 intercept -1.871 -1.892 -1.864 E Urban School -0.038 -0.040 -0.028 V Rural School 0.048 0.048 0.044 E Private School 0.871 *** 0.874 *** 0.846 *** L Total Enrollment 0.000 *** 0.000 *** 0.000 *** % Asian 0.693 + 0.710 + 1.474 * 3 % Hispanic -0.617 + -0.647 + -0.772 + % Black 0.100 0.058 0.301 V % Single Parent -6.138 -6.471 -6.354 A % LEP 0.747 0.569 -0.433 R % on Free Lunch -0.060 -0.072 -0.056

L % Black 0.086 0.064 0.206 E % Asian -0.256 -0.283 -0.416 V % Hispanic -0.252 -0.325 -0.738 E % Foreign Born 0.376 0.456 0.558 L % Female Households -0.700 -0.759 -0.739 2 Economic Distress -0.062 -0.064 -0.044 SES 0.422 *** 0.368 *** 0.372 *** RACE Asian 0.505 *** 0.493 *** 0.475 *** * % Asian in School -1.117 * % Hisp in School 1.006 * L * % Black in School -0.117 E * % Black in Nbhd -0.984 V * % Asian in Nbhd 1.993 E * % Hisp in Nbhd -1.199 L Hispanic -0.002 0.032 0.027 * % Asian in School -0.018 * % Hisp in School -0.104 * % Black in School -1.084 * % Black in Nbhd -1.932 1 * % Asian in Nbhd -2.453 * % Hisp in Nbhd 0.652 Black -0.317 * -0.306 * -0.347 * * % Asian in School -7.508 ** * % Hisp in School -0.040 V * % Black in School -0.630 A * % Black in Nbhd -0.308 R * % Asian in Nbhd -9.542 S * % Hisp in Nbhd -2.375 FEMALE 0.499 *** 0.491 *** 0.486 *** FAMILY STRUCTURE Single Parent Family 0.158 + 0.161 + Other Family Form -0.068 -0.064 Test Scores 0.015 *** 0.015 ***

80

Table 4.13 – Regression Coefficients for GPA in 12th Grade Exogenous Base Model Interactions

L Level 3 intercept 5.770 5.840 5.850 E Urban School 0.105 0.149 0.153 V Rural School 0.144 0.128 0.141 E Private School 0.606 *** 0.556 *** 0.531 *** L Total Enrollment 0.000 + 0.000 * 0.000 * % Asian 0.055 0.200 2.182 3 % Hispanic 0.054 -0.195 -0.436 % Black -0.274 -0.670 ** -0.693 * V % Single Parent -15.641 ** -14.661 * -14.324 * A % LEP -2.671 -1.464 -2.066 R % on Free Lunch -0.839 *** -0.856 *** -0.849 ***

L % Black -0.075 -0.309 -0.278 E % Asian -0.352 0.069 -0.578 V % Hispanic -0.857 -0.871 -0.194 E % Foreign Born 1.085 0.897 0.924 L % Female Households -0.203 -0.674 -0.592 2 Economic Distress -0.082 -0.038 -0.053

SES 0.903 *** 0.259 *** 0.261 *** RACE Asian 1.084 *** 0.813 *** 0.894 *** * % Asian in School -2.563 * L * % Hisp in School 0.720 E * % Black in School 0.762 V * % Black in Nbhd 0.965 E * % Asian in Nbhd 2.878 L * % Hisp in Nbhd -0.491

Hispanic -0.371 *** -0.078 -0.097 * % Asian in School -2.086 * % Hisp in School 0.311 * % Black in School -0.027 * % Black in Nbhd -1.562 * % Asian in Nbhd -4.223 * % Hisp in Nbhd -1.770

Black -0.831 *** -0.286 ** -0.331 ** 1 * % Asian in School -4.332 * * % Hisp in School -0.349 * % Black in School -0.114 * % Black in Nbhd 0.057 * % Asian in Nbhd 1.558 * % Hisp in Nbhd -0.856

V FEMALE 0.656 *** 0.552 *** 0.550 *** A FAMILY STRUCTURE R Single Parent Family -0.362 *** -0.361 *** S Other Family Form -0.421 *** -0.418 ***

Test Scores 0.141 *** 0.141 ***

EXTRACURRICULARS Any Participation? Total Participation CATEGORIES Hi Profile Sports Low Profile Sports Cheerleading / Pom Fine Arts Academic Clubs Student Government Occupational Clubs Social Activities

81

Table 4.14 – Regression Coefficients for GPA in 12th Grade, with Extracurriculars Xcurrs Total Xcurrs Categories

L Level 3 intercept 5.456 5.469 5.497 5.503 5.500 5.506 E Urban School 0.155 0.157 0.149 0.152 0.140 0.143 V Rural School 0.126 0.139 0.110 0.123 0.143 0.155 + E Private School 0.537 *** 0.512 *** 0.482 *** 0.458 *** 0.466 *** 0.443 *** L Total Enrollment 0.000 + 0.000 + 0.000 0.000 0.000 0.000 % Asian 0.248 2.179 + 0.252 2.145 + 0.288 2.084 + 3 % Hispanic -0.209 -0.450 -0.162 -0.405 -0.160 -0.409 % Black -0.628 ** -0.657 * -0.604 ** -0.655 * -0.573 ** -0.619 * V % Single Parent -13.736 * -13.430 * -11.875 + -11.583 + -12.229 * -12.025 * A % LEP -1.243 -1.865 -1.141 -1.773 -1.627 -2.237 R % on Free Lunch -0.837 *** -0.831 *** -0.839 *** -0.833 *** -0.777 *** -0.772 ***

L % Black -0.361 -0.357 -0.350 -0.347 -0.301 -0.266 E % Asian 0.105 -0.537 0.227 -0.781 0.300 -0.334 V % Hispanic -0.908 -0.363 -0.930 -0.409 -0.984 -0.379 E % Foreign Born 0.977 1.021 0.897 0.980 0.867 0.932 L % Female Households -0.460 -0.419 -0.441 -0.419 -0.388 -0.359 2 Economic Distress -0.032 -0.047 -0.020 -0.036 -0.029 -0.045

SES 0.231 *** 0.233 *** 0.182 *** 0.184 *** 0.173 *** 0.175 *** RACE Asian 0.797 *** 0.891 *** 0.742 *** 0.835 *** 0.702 *** 0.792 *** * % Asian in School -2.540 * -2.497 ** -2.382 * L * % Hisp in School 0.693 0.779 + 0.720 + E * % Black in School 0.828 0.847 0.787 V * % Black in Nbhd 1.031 1.149 0.919 E * % Asian in Nbhd 2.688 3.237 2.627 L * % Hisp in Nbhd -0.456 -0.337 -0.462

Hispanic -0.069 -0.089 -0.102 -0.114 -0.115 -0.132 * % Asian in School -1.937 -1.893 -1.709 * % Hisp in School 0.332 0.314 0.349 * % Black in School 0.083 0.064 0.116 * % Black in Nbhd -1.454 -1.401 -1.377 * % Asian in Nbhd -3.600 -3.407 -3.893 * % Hisp in Nbhd -1.541 -1.583 -1.707

Black -0.304 ** -0.336 ** -0.327 *** -0.367 *** -0.315 *** -0.346 ** 1 * % Asian in School -3.540 + -3.371 + -3.172 * % Hisp in School -0.334 -0.337 -0.280 * % Black in School -0.111 -0.052 -0.064 * % Black in Nbhd 0.178 0.201 0.112 * % Asian in Nbhd 1.372 2.459 1.985 * % Hisp in Nbhd -0.286 -0.004 -0.199

V FEMALE 0.545 *** 0.543 *** 0.491 *** 0.490 *** 0.501 *** 0.499 *** A FAMILY STRUCTURE R Single Parent Family -0.355 *** -0.354 *** -0.353 *** -0.352 *** -0.350 *** -0.350 *** S Other Family Form -0.408 *** -0.405 *** -0.393 *** -0.389 *** -0.405 *** -0.401 ***

Test Scores 0.139 *** 0.139 *** 0.136 *** 0.136 *** 0.133 *** 0.133 ***

EXTRACURRICULARS Any Participation? 0.476 *** 0.468 *** Total Participation 0.219 *** 0.218 *** CATEGORIES Hi Profile Sports 0.110 ** 0.111 ** Low Profile Sports 0.281 *** 0.277 *** Cheerleading / Pom 0.078 0.081 Fine Arts -0.015 -0.010 Academic Clubs 0.548 *** 0.546 *** Student Government 0.408 *** 0.406 *** Occupational Clubs -0.057 -0.060 Social Activities 0.199 *** 0.196 ***

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Table 4.15 – Regression Coefficients for Odds of High School Graduation Exogenous Base Model Interactions

L Level 3 intercept 3.613 4.078 3.724 E Urban School -0.431 * -0.418 * -0.354 ** V Rural School 0.000 0.035 0.024 E Private School 1.595 *** 1.430 *** 1.210 *** L Total Enrollment 0.000 + 0.000 + 0.000 * % Asian -0.562 -0.465 -0.849 3 % Hispanic -0.241 -0.424 -0.529 % Black -0.043 -0.147 -0.269 V % Single Parent -24.910 * -20.617 + -17.475 ** A % LEP -3.732 -1.198 -0.802 R % on Free Lunch -1.146 ** -1.218 *** -1.139 ***

L % Black 1.039 0.823 0.765 E % Asian 4.635 5.908 * 7.129 V % Hispanic 0.446 1.044 -0.431 E % Foreign Born 0.398 -0.273 -0.588 L % Female Households 0.321 0.825 0.686 2 Economic Distress -0.282 -0.289 -0.217 +

SES 0.651 *** 0.213 + 0.196 ** RACE Asian 1.716 *** 1.546 *** 1.309 *** * % Asian in School 5.045 *** L * % Hisp in School -0.123 E * % Black in School 5.585 *** V * % Black in Nbhd -2.944 ** E * % Asian in Nbhd 2.659 L * % Hisp in Nbhd -8.102 ***

Hispanic 0.066 0.125 0.046 * % Asian in School 0.419 * % Hisp in School 0.231 * % Black in School -0.744 * % Black in Nbhd -0.522 * % Asian in Nbhd 0.173 * % Hisp in Nbhd 2.621

Black -0.372 -0.035 -0.193 1 * % Asian in School -4.347 + * % Hisp in School 0.429 * % Black in School 0.508 * % Black in Nbhd -0.179 * % Asian in Nbhd -2.534 * % Hisp in Nbhd 4.659 +

V FEMALE 0.636 *** 0.595 *** 0.514 *** A FAMILY STRUCTURE R Single Parent Family -1.003 *** -0.832 *** S Other Family Form -0.932 *** -0.783 ***

Test Scores 0.075 *** 0.064 ***


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Table 4.16 – Regression Coefficients for Odds of HS Graduation, with Extracurriculars Xcurrs Total Xcurrs Categories

L Level 3 intercept 3.599 3.269 4.215 3.293 3.573 3.294 E Urban School -0.392 + -0.339 ** -0.419 * -0.346 ** -0.398 * -0.342 ** V Rural School 0.049 0.026 0.054 0.003 0.061 0.042 E Private School 1.435 ** 1.198 *** 1.417 ** 1.167 *** 1.271 ** 1.104 *** L Total Enrollment 0.000 0.000 + 0.000 0.000 0.000 0.000 % Asian -0.153 -0.768 -0.280 -0.811 -0.368 -0.866 3 % Hispanic -0.448 -0.544 -0.459 -0.550 -0.470 -0.622 % Black -0.074 -0.196 -0.112 -0.223 0.018 -0.105 V % Single Parent -17.493 -14.725 * -20.478 + -13.522 * -17.603 -14.798 * A % LEP -1.660 -0.961 -0.892 -0.633 -0.974 -0.754 R % on Free Lunch -1.212 ** -1.170 *** -1.216 ** -1.172 *** -1.135 ** -1.098 ***

L % Black 0.810 0.700 1.001 0.678 0.780 0.751 E 6.195 + 7.594 6.051 + 7.413 6.193 * % Asian 7.271 V % Hispanic 1.218 -0.655 1.120 -0.714 1.219 -0.698 E % Foreign Born -0.632 -0.699 -0.503 -0.854 -0.540 -0.671 L % Female Households 0.984 0.689 0.723 0.704 0.820 0.573 2 Economic Distress -0.286 -0.209 -0.307 -0.211 -0.272 -0.212

SES 0.187 0.161 * 0.131 0.128 + 0.111 0.101 RACE Asian 1.511 ** 1.352 *** 1.499 ** 1.257 *** 1.444 ** 1.221 *** * % Asian in School 5.329 *** 5.229 *** 5.504 *** L * % Hisp in School -0.203 -0.089 -0.046 E * % Black in School 6.058 *** 5.426 *** 5.338 *** V * % Black in Nbhd -3.623 *** -3.581 *** -3.902 *** E * % Asian in Nbhd 2.756 2.460 3.272 L * % Hisp in Nbhd -8.671 *** -8.271 *** -8.977 ***

Hispanic 0.120 0.027 0.102 0.034 0.123 0.017 * % Asian in School 0.679 0.654 0.646 * % Hisp in School 0.293 0.254 0.361 * % Black in School -0.701 -0.745 -0.786 * % Black in Nbhd -0.260 -0.416 -0.291 * % Asian in Nbhd 1.156 0.834 1.237 * % Hisp in Nbhd 3.222 + 3.215 + 3.149 +

Black -0.092 -0.222 -0.102 -0.279 -0.123 -0.268 1 * % Asian in School -3.689 -3.354 -3.137 * % Hisp in School 0.460 0.364 0.448 * % Black in School 0.493 0.604 0.532 * % Black in Nbhd -0.008 0.083 0.054 * % Asian in Nbhd -1.959 -1.193 -1.709 * % Hisp in Nbhd 5.953 * 6.373 * 6.570 *

V FEMALE 0.610 *** 0.524 *** 0.559 *** 0.491 *** 0.580 *** 0.498 *** A FAMILY STRUCTURE R Single Parent Family -1.013 *** -0.834 *** -1.030 *** -0.833 *** -1.007 *** -0.850 *** S Other Family Form -0.937 *** -0.771 *** -0.931 *** -0.764 *** -0.924 *** -0.778 ***

Test Scores 0.073 *** 0.062 *** 0.069 *** 0.062 *** 0.070 *** 0.061 ***

EXTRACURRICULARS Any Participation? 0.676 *** 0.615 *** Total Participation 0.340 *** 0.341 *** CATEGORIES Hi Profile Sports 0.486 ** 0.445 *** Low Profile Sports 0.620 *** 0.525 *** Cheerleading / Pom 0.948 * 0.952 *** Fine Arts 0.240 0.199 * Academic Clubs 0.460 ** 0.396 *** Student Government -0.056 -0.080 Occupational Clubs -0.263 -0.174 + Social Activities 0.846 ** 0.707 ***

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Table 4.17 – Regression Coefficients for Odds of Attending Postsecondary Education Exogenous Base Model Interactions

L Level 3 intercept 1.650 1.821 1.819 E Urban School -0.221 + -0.236 + -0.212 + V Rural School -0.236 * -0.249 * -0.219 * E Private School 1.952 *** 1.873 *** 1.813 *** L Total Enrollment 0.000 ** 0.000 ** 0.000 ** % Asian 0.732 0.879 3.021 + 3 % Hispanic -0.325 -0.541 + -0.873 * % Black 0.277 -0.002 0.077 V % Single Parent -27.007 *** -26.947 *** -26.368 *** A % LEP 6.686 + 7.510 * 7.251 * R % on Free Lunch -0.916 *** -0.961 *** -0.926 ***

L % Black 0.451 0.311 0.652 E % Asian -2.796 -2.273 -6.030 V % Hispanic -0.550 -0.607 -0.969 E % Foreign Born 1.846 1.843 1.887 L % Female Households -1.869 -1.767 -1.618 2 Economic Distress -0.083 -0.112 -0.112

SES 1.115 *** 0.795 *** 0.786 *** RACE Asian 1.064 *** 0.963 *** 0.804 *** * % Asian in School -2.960 L * % Hisp in School -0.224 E * % Black in School -0.467 V * % Black in Nbhd 0.406 E * % Asian in Nbhd 6.491 + L * % Hisp in Nbhd 0.305

Hispanic 0.090 0.234 + 0.135 * % Asian in School -2.884 * % Hisp in School 0.434 * % Black in School -0.725 * % Black in Nbhd -0.293 * % Asian in Nbhd 3.050 * % Hisp in Nbhd 0.687

Black 0.082 0.330 * 0.412 * 1 * % Asian in School 6.220 * % Hisp in School 0.377 * % Black in School 0.202 * % Black in Nbhd -0.804 * % Asian in Nbhd 9.260 * % Hisp in Nbhd -1.770

V FEMALE 0.450 *** 0.400 *** 0.393 *** A FAMILY STRUCTURE R Single Parent Family -0.061 -0.058 S Other Family Form -0.421 *** -0.407 ***

Test Scores 0.085 *** 0.083 ***


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Table 4.18 – Regression Coefficients for Odds of Attending PSE, with Extracurriculars Xcurrs Total Xcurrs Categories

L Level 3 intercept 1.287 1.287 1.891 1.333 1.306 1.304 E Urban School -0.233 + -0.209 + -0.246 + -0.210 + -0.241 + -0.218 + V Rural School -0.256 * -0.225 * -0.251 * -0.251 ** -0.236 * -0.206 * E Private School 1.870 *** 1.810 *** 1.868 *** 1.761 *** 1.771 *** 1.731 *** L Total Enrollment 0.000 *** 0.000 *** 0.000 ** 0.000 *** 0.000 *** 0.000 *** % Asian 0.999 3.053 * 0.937 3.041 + 0.931 2.693 + 3 % Hispanic -0.590 + -0.919 * -0.561 + -0.877 * -0.563 + -0.902 * % Black 0.089 0.162 0.045 0.158 0.150 0.231 V % Single Parent -25.383 *** -24.926 *** -27.495 *** -23.458 *** -24.568 *** -24.129 *** A % LEP 8.240 * 7.907 * 8.424 * 8.854 * 7.903 * 7.764 * R % on Free Lunch -0.963 *** -0.929 *** -0.977 *** -0.961 *** -0.876 *** -0.856 ***

L % Black 0.102 0.460 0.249 0.378 0.081 0.390 E % Asian -2.094 -6.268 + -2.438 -7.244 + -2.337 -6.962 + V % Hispanic -0.589 -1.285 -0.585 -1.300 -0.677 -1.323 E % Foreign Born 1.832 1.931 1.905 1.921 1.774 1.814 L % Female Households -1.275 -1.314 -1.518 -1.330 -1.543 -1.441 2 Economic Distress -0.104 -0.099 -0.122 -0.090 -0.082 -0.084

SES 0.766 *** 0.757 *** 0.715 *** 0.710 *** 0.698 *** 0.690 *** RACE Asian 0.945 *** 0.819 *** 0.898 *** 0.754 *** 0.873 *** 0.757 *** * % Asian in School -2.902 -2.920 -2.543 L * % Hisp in School -0.314 -0.258 -0.230 E * % Black in School -0.480 -0.571 -0.555 V * % Black in Nbhd 0.556 0.560 0.609 E * % Asian in Nbhd 6.224 7.553 * 7.424 * L * % Hisp in Nbhd 0.101 0.210 0.582

Hispanic 0.247 + 0.147 0.208 0.141 0.232 + 0.136 * % Asian in School -2.701 -2.844 -2.416 * % Hisp in School 0.470 0.392 0.447 * % Black in School -0.622 -0.670 -0.669 * % Black in Nbhd -0.028 -0.155 0.072 * % Asian in Nbhd 4.832 5.451 5.165 * % Hisp in Nbhd 1.200 1.100 1.199

Black 0.280 + 0.394 * 0.275 + 0.348 * 0.255 + 0.364 * 1 * % Asian in School 7.434 6.998 7.817 * % Hisp in School 0.358 0.250 0.275 * % Black in School 0.191 0.247 0.198 * % Black in Nbhd -0.701 -0.720 -0.677 * % Asian in Nbhd 9.072 9.964 8.399 * % Hisp in Nbhd -0.876 -0.539 -0.645

V FEMALE 0.408 *** 0.400 *** 0.361 *** 0.351 *** 0.380 *** 0.377 *** A FAMILY STRUCTURE R Single Parent Family -0.046 -0.044 -0.066 -0.032 -0.031 -0.031 S Other Family Form -0.399 *** -0.389 *** -0.401 *** -0.380 *** -0.392 *** -0.377 ***

Test Scores 0.082 *** 0.080 *** 0.080 *** 0.079 *** 0.079 *** 0.077 ***

EXTRACURRICULARS Any Participation? 0.689 *** 0.679 *** Total Participation 0.268 *** 0.335 *** CATEGORIES Hi Profile Sports 0.330 *** 0.336 *** Low Profile Sports 0.602 *** 0.578 *** Cheerleading / Pom 0.308 * 0.307 * Fine Arts 0.341 *** 0.333 *** Academic Clubs 0.405 *** 0.395 *** Student Government 0.681 *** 0.636 *** Occupational Clubs -0.143 -0.137 Social Activities 0.324 ** 0.309 ***

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Table 4.19 – Significant Interactions Between Neighborhood Racial Composition and Individual Race on the Odds of Extracurricular Participation

INDIVIDUAL NEIGHBORHOOD

TYPE RACE Asian Hispanic Black

Outcome Coefficient Coefficient Coefficient Asian

Hi Profile Sports -5.6 + Cheerleading -11.8 6.7 Fine Arts -8.2 -4.3 -2.4

Hispanic General Participation -3.8 Total Participation -3.7 + Cheerleading -4.5 Fine Arts -9.3 Occupational Activities -7.6 -3.7

Black General Participation -6.9 -1.7 Total Participation -3.7 Low Profile Sports -1.5 + Cheerleading -6.5 Fine Arts -6.5 Occupational Activities -16.0 -6.4

+ p <.10; all others p <.05 or greater

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Table 4.20 – Significant Interactions Between School Racial Composition and Individual Race on the Odds of Extracurricular Participation

INDIVIDUAL SCHOOL TYPE RACE Asian Hispanic Black

Outcome Coefficient Coefficient Coefficient Asian

General Participation -1.6 Low Profile Sports -2.9 Cheerleading 3.5 Fine Arts -2.0 Occupational Activities 1.0+ Social Activities 1.0

Hispanic Low Profile Sports -2.6+ Cheerleading 4.5 Academic Clubs -2.6 Occupational Activities -4.1

Black General Participation -9.0 Total Participation -4.4 Low Profile Sports -8.9 Fine Arts -7.6 Academic Clubs -8.5 Occupational Activities -0.9 Social Activities -7.5

+ p <.10; all others p <.05 or less

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1 Occupational Club Participation had a negative effect in both models, but only significant at the p <.10 level in the interaction model. In the straight outcome model p=.119. Such participation reduced the odds of graduating by 16 – 23%, depending on which model used.

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DISCUSSION

The two key questions of this study are: 1) Do neighborhood and school contexts

influence extracurricular participation? and 2) Does extracurricular participation affect

educational achievement and attainment, controlling for these other influences? The answers

appear to be 1) yes, but not as much as expected, and 2) yes.

DO NEIGHBORHOOD AND SCHOOL CONTEXTS INFLUENCE EXTRACURRICULAR PARTICIPATION?

The Effects of Neighborhood Context. Neighborhood of residence had few direct effects on the

odds a student would be involved in the extracurriculum. Racial composition of the

neighborhood had almost no impact on patterns of participation, significantly affecting only Fine

Arts and Student Government involvement (both increased in “blacker” neighborhoods), while

social class measures had more consistent effects. Neighborhoods characterized by higher levels

of economic distress (the combination of unemployment and very low income families in the

neighborhood) depressed the number of activities a student would be involved in, the odds of

being on cheerleading or pom pom teams, or of taking part in drama or music (Fine Arts)

activities. Similarly, neighborhoods composed largely of female headed households reduced the

odds of taking part in student government and occupational activities. The proportion of foreign

born residents also had important consequences for extracurricular participation, in that these

neighborhoods lessened the likelihood of being involved in High Profile Sports and Cheerleading

/ Pom Team.

These results make sense, in that economic distress signals, in part, a shortfall in the

resources available for supporting participation in extracurricular activities. Female headed

households also typically lack material and financial resources. Athletic shoes, instrument rental

/ purchase, materials, even time, are all in shorter supply for poorer families, and the social

norms for participation (or for non-participation) among one’s neighbors would reflect such lack

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of resources. When none of the neighbor kids – presumably the first friends you make - are

involved in activities, why would you be involved? Few of the families living in the area are able

to support extracurricular involvement, so there is no precedent for it. Plus, none of the friends a

student has are taking part, so there are no social demands / attractions for it.

There may also be competing demands that inhibit one’s ability to take part in the

extracurriculum, even if the desire exists to do so. Demands for high schoolers to get part time

jobs to help support the family are greater among poorer families, and, in a neighborhood of

poorer families, an ethos opposed to “frivolous” pursuits like school sports or drama may

develop, while the expectation of getting a job instead becomes predominant. Even if formal

employment is not expected, other expectations like household chores or babysitting of younger

siblings may come to be expected. Parents may say things like “If your friend down the street

pitches in, why shouldn’t you?” Neighborhood residents constitute a reference group that serves

as a model for behavior, activities and expectations, as well as provide and reinforce norms

regarding which pursuits are worthwhile, which are not, and which should be pursued and which

should not.

Hypothesis 3 proposes that “Neighborhood disadvantage will lead to lower rates of

participation in all measures of extracurriculars.” In many people’s minds, and in many studies

(including this one), one measure of disadvantage is the racial composition of the neighborhood,

specifically the rates of minority residence therein. “More minorities” tends to translate to “more

disadvantage”, and, given the negative correlation between minority racial status and SES, it is

an understandable link. But the results of this study reveal that it is not the racial composition of

the neighborhood but, rather, the material resources of the neighborhood – e.g., employment and

income – that represent the important consideration. Disadvantage does lead to lower rates of

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participation, when disadvantage is measured by economic distress or the proportion of female-

headed households. Indeed, all of the significant effects – even at the p <.10 level – for

increasing proportions of minority residents are positive: kids from minority neighborhoods are

more likely to take part in the extracurriculum, holding the social class measures of the

neighborhood constant.

There are also relatively few significant interactions between neighborhood race and

individual race, but enough to lend support to the idea that neighborhood context does matter,

and it matters differently for different students. The way in which neighborhood matters depends

on the race of the student, especially if the student is black. Significant interactions between

neighborhood racial composition and black students both were the most common and had the

largest magnitudes, as shown in Table 4.17.

Black students are generally more involved in the extracurriculum than white students, so

why are they much less likely to be as involved if they live in a neighborhood with more

minorities than their schoolmates? One is tempted to answer that it relates to the fact that the

minority neighborhoods are probably also the poorer neighborhoods, but SES of the

neighborhood is controlled.1 It may have more to do with the standing of such neighborhoods

within the social hierarchy of the school. The “in-crowd” (those students with the most social

influence over the school) may see “high minority” areas as less desirable, and thus discourage

participation by students from such areas. Furthermore, the students from these neighborhoods

may take an “us-versus-them” attitude, essentially boycotting activities seen as mainstream

within the school, reflecting a sense of estrangement from the institution and those within it who

hail from “the better side of town.” As Ogbu (1990) notes, the way in which a minority group

views its standing in a society “affects both their adaptation to minority status and their

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adaptation to schooling” (p. 46). Taking part in school activities may be seen as an example of

“acting white” (despite the fact that minority students are often more likely to take part), and

such a designation serves as a strong deterrent for students, especially when they have to go

home to a neighborhood where they may be harassed (or worse) for their participation (cf. Farkas

et al 2002).

Similar arguments can be made for Hispanic and Asian students, though other factors

probably also contribute to their lowered participation, most importantly cultural and linguistic

differences. Students from neighborhoods of largely Hispanic and Asian residents most likely

attend school in an environment very different from that to which they are accustomed at home

and in their neighborhood (Ogbu 1990; Philips 1976). These differences may cause them

difficulty in school, normally seen as grades and achievement, but can be generalized to include

extracurricular participation. Furthermore, the comments made earlier regarding poorer

neighborhoods and the ethos of non-participation that can exist due to economic circumstances

can be extended here as well, in that neighborhood cultural circumstances like norms against

playing sports or being involved in fine arts activities may discourage participation.

The Effects of School Context. Schools had more effects on the participation patterns of students

than did neighborhood of residence. Similar to the results for neighborhoods, the measures of

socioeconomic status in the models more frequently proved significant than the racial

composition variables. The proportion of kids from single-parent families was the largest

influence on participation rates, greatly reducing the odds of any participation (and of three of

the categories of activities) and the total number of activities, while attending a private school

increased the odds of participation (by 46%) and the number of activities in which a student

participates. In addition, the proportion of students on free lunch had a negative effect on playing

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Low Profile Sports (generally, the more expensive sports to take part in, including such “country

club sports” as golf, tennis, and swimming, as well as more middle-class pursuits like soccer and

hockey). These sports (with the exception of soccer) involve substantial costs for equipment and

playing time (renting ice time for hockey, greens fees for golf, etc.). Thus, it is not surprising to

find lower levels of participation in these areas in schools with larger proportions of poor kids.

It is also not surprising to find that a student body of lower SES overall has lower

participation rates. As noted frequently in the literature (cf. Farkas, Lleras, and Maczuga 2002), a

negative orientation towards things scholastic (including school-sponsored activities) is common

in lower SES schools. Students of lower SES have fewer resources to support them in

participating, as noted in the neighborhood discussion. Like living in a neighborhood of poorer

families, attending school where many or most of the other students have too few resources to

take part and / or there is an ethos opposed to participation (for the sorts of reasons given earlier)

serves to lessen the likelihood that a student will be involved. Furthermore, schools with a lower-

SES student body overall are fed by neighborhoods of lower SES, where school funding is often

in dire straights, and one of the first items to see drastic cuts in times of financial hardship is the

extracurriculum. Schools with a lower SES catchment area may not be able to offer as many

activities, reducing the opportunities for participation for their students.

The one area where lower SES leads to higher rates of participation is the area of

extracurriculars that moves students concretely along the employment path: more students on

free lunch in a school led to higher odds of taking part in Occupational Activities. This no doubt

reflects the importance of finding employment for students whose families are of lower SES.

Within a school where many kids are in such circumstances, there naturally would be a

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commonly-held disposition favoring participation in activities that would (at least seemingly)

help a student find employment after high school.

School size and urbanicity also had significant effects. Total enrollment had a positive

and significant effect in every model predicting extracurricular participation, although the

coefficient was very small. This is due to the scale of the variable. School size was measured as

“number of students”, thus the coefficient reflects the change in the odds of participation for

every additional student. Therefore, it is easy to see why the magnitude of the coefficient is so

small. Future analyses of these data will modify the scale of measurement on this variable to the

“100s of students” level, thereby making the coefficients more readily interpretable.

Methodological issues aside, the positive effects of this measure indicate the greater wealth of

extracurricular opportunities available in larger schools. As Shouse (2004) notes, larger schools

have a deeper pool of interests and potential participants to draw upon, therefore they can offer a

wider range of activities that will draw sufficient numbers of students to justify the offerings. In

other words, a student in a larger school is more likely to find a club or activity that meets their

interests, thereby increasing the probability that the student will be involved in a school-

sponsored activity. Larger schools had no significant effects on student grades or odds of high

school graduation, contrary to the “small schools” movement currently popular in the United

States. The important consideration here is that the racial and social class composition of the

schools is controlled. Lower social class across the student body is associated with lower grades

and lower odds of graduation. Because lower social class schools and larger schools tend to be

concentrated in urban areas, these two factors are often conflated by “small school” advocates.

The results found here indicate that the relationship between larger schools and poorer

educational achievement and attainment is a spurious relationship, one that is actually explained

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not be school size, but by social class composition. Larger schools actually have significant

positive effects on the odds of attending postsecondary education. Larger schools have more

students to offer colleges, thus they draw attention from college recruiters, plus they are located

in urban areas where more postsecondary institutions are located, facilitating contact between

high schools (students, guidance counselors, etc.) and the higher education system in general.

Urbanicity of the school (i.e., whether the school is located in an urban, suburban, or rural

area) also has significant effects. Students in urban schools are less likely to play High Profile

Sports than students in suburban schools (the reference category), likely due to the need for

larger open green space and for extensive and expensive equipment for football and baseball /

softball (two of the three sports that make up the category), resources in short supply in urban

schools.2 The only other significant effect of urbanicity for the extracurricular outcomes is the

positive effect of rural schools on participation in occupational activities. This no doubt reflects

the practical orientation of rural areas, where students are encouraged to pursue activities that

will lead to clear occupational outcomes, especially ones immediately after finishing high school.

Urbanicity had no effect on grades, but, consistent with well-known trends, students in urban

schools are less likely to graduate than suburban students, while rural students show no

difference from suburbanites. Going on to college is less likely for rural students (and for urban

students, if the criteria for significance is relaxed to p<.10) than suburban ones, again, reflecting

the rural orientation towards employment following school.

As noted in the Results section, the interaction between school racial composition and

individual race had more consistent results than the interaction between neighborhood racial

composition and individual race. Asian schools consistently depressed the odds of participation

for Hispanic and black students (only Hispanic cheerleading odds were increased). This may be a

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result of two phenomena: Asian students’ greater propensity for participation (see findings

discussed earlier) may leave less room for others to take part in schools with larger numbers of

Asian students; and a reluctance to participate among other minority students may result from the

view of Asian students as more incorporated into the school and a desire to differentiate oneself

from members of “another group.”

Asian students tend to be more integrated into the school, reflected both in their academic

achievement – which outpaces other minority groups, as well as white students (Kao 1995;

Tsang 1984) – and in their greater extracurricular participation, as shown by results discussed

earlier in this work. Such integration (among other things) marks Asian students as the “model

minority,” setting them apart from other students (Wong, Lai, Nagasawa, and Lin 1998). This

may reflect one of two things (or a combination of both). As noted earlier, Asian students tend to

be more involved in the extracurriculum. In a school of higher Asian composition, this may

produce a situation where other minority students are essentially “closed out” of many activities,

as these activities are dominated by Asian students, leaving fewer “slots” for others.

Alternatively, the activities come to be perceived as undesirable for participation, with black and

Hispanic students seeing these activities as “white” or “Asian” activities and therefore not

wanting to take part. Students who are of other groups will seek ways to distinguish themselves

and reinforce their own group membership, as demonstrated by such scholars as Coleman

(1961), Willis (1977) , Macleod (1987), Eckert (1989), and Farkas, et al (2002). Thus, black or

Hispanic students, as a group, may not participate in school-sponsored activities as a way of

reinforcing their friendships with other black or Hispanic students with whom they may feel

more connected, being of the same race and in a school where they are not only minorities, but

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where another minority makes up a larger share of the student body than their own group and is

strongly inclined toward participation.

As in the discussion of neighborhood, the social group around the student in the school

develops certain norms regarding participation, and these norms have a strong influence on the

activity choices a student makes. In the neighborhood, neighbors are the relevant group; in the

school the immediate peer group is the most important, and friendship patterns tend to be within-

race (Hallinan and Williams 1989). Furthermore, Hallinan and Williams (1989) also found a

high level of consistency between friends in their activity choices. In other words, friends

participate in activities together, or, conversely, students do not take part in things in which their

friends are not involved. Thus extracurricular participation (or the lack thereof) becomes an

example of solidarity within friendship circles, which are generally within the same race. Thus,

black students in a school with lots of Asian kids may observe the high levels of involvement of

the Asian kids, and, in turn, not participate as a way of demonstrating their own uniqueness and

solidarity as a group.

Overall, school and neighborhood racial context did not overwhelmingly produce the

anticipated results, in that racial composition had little effect on participation, either directly or

when interacted with the race of individual students. Living in any minority neighborhood

(whether Asian, Hispanic, or black) generally depressed the odds of participation for all students

(regardless of their individual race), and only Asian schools had consistent (and detrimental)

effects, and only for Hispanic and black students.

DOES EXTRACURRICULAR PARTICIPATION AFFECT EDUCATIONAL ACHIEVEMENT AND ATTAINMENT, CONTROLLING FOR THE INFLUENCES OF NEIGHBORHOOD AND SCHOOL CONTEXT? The answer to the second research question is more definitive. Across the board,

measures of extracurricular participation produced significant, positive effects on student grades,

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the odds of graduating high school, and the odds of attending some form of postsecondary

education (PSE). Noteworthy results related to school and neighborhood context are also evident

in the models presented here, and will be discussed in addition to covering the results bearing

specifically on the research question.

The Effects of Extracurricular Participation on Educational Achievement and Attainment. As

hypothesized, participating in extracurricular activities during the 10th grade has a positive

impact on the average grades a student receives in the 12th grade. The coefficients for all the

predictors other than extracurricular activities stay relatively constant from the Base Model

through the final interaction model with all eight categories of activities included. This suggests

that extracurriculars have their own, discrete effect on grades, independent of student race, class

gender, or scholastic ability (measured by test score), and controlling for neighborhood and

school context. None of the effects of extracurriculars was in the negative direction, even those

for “Occupational Clubs” which could be claimed to direct student attention away from

“academic” pursuits and towards vocational ones. Instead, participants in these activities (along

with those in Cheerleading and Fine Arts activities) had no significant differences with non-

participants.

A common objection to this result is one of selection bias: students who are more

talented, and / or who come from “better” families or families of greater financial means, are the

students who participate in extracurriculars. This objection is not valid in these analyses, given

the controls for Standardized Test Scores (controlling for ability), socioeconomic status (SES),

and family structure. The findings are also sound in the face of claims of teacher favoritism

leading to inflated grades. This claim is usually made in regard to athletes, but the strongest

effects were not for athletes, but for student leaders (in student government) and academic club

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members. Indeed, High Profile Sports (those usually most associated with claims of favoritism,

like football or basketball) showed the smallest significant effect on grades. This longitudinal

design, combined with the extensive controls for selection factors and confounding influences,

provides strong evidence that participation has a unique and independent positive effect on the

achievement levels of students.

This effect may manifest itself for a number of reasons. First, a perspective informed by

social capital theory presents a strong argument that the links between participants and other

individuals around them as a result of their participation may be an important mechanism for

understanding the positive effects of participation. As Hanks and Eckland (1976) showed,

students involved in extracurriculars tend to be more achievement-oriented and have greater

aspirations for further educational attainment (i.e., plans for college.) Thus, participation places a

student within a social milieu consisting of peers who encourage academic achievement, who

can support each other in terms of studying and completing class assignments, and who value

such accomplishments. Furthermore, the adults who supervise these activities extend this support

and reinforcement for adhering to school norms of achievement in the classroom and working

towards greater levels of attainment. This fortifies the expectations of such efforts, as well as

facilitating the accomplishment of such goals. Contact between students participating in

extracurricular activities and adults – teachers, parents, other adults – is increased as a result of

participation (Broh 2002). Therefore, more contact with both peers and “positive role model”

adults may explain much of the effect of participation on grades. Other forces may also lead to

improved achievement, most notably the increased efforts in the classroom some students may

exhibit in order to maintain their eligibility to participate in their favorite activities. To be

allowed to participate in extracurricular activities, most schools require that students satisfy a

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minimum GPA standard. Coaches or club advisors may do the same even in the absence of

institutional requirements. Finally, students may learn generalizable skills like organization, goal

setting, time management, and persistence as a result of participation in activities. These skills

can translate to the classroom, enabling students to better manage their scholastic demands and

achieve better grades.

Clearly, as shown in the tables at the end of the last chapter, extracurricular participation

has beneficial effects on educational attainment, as well as achievement. Having participated in

any activity at all increased the odds of graduation by 85% or more, and doubles the odds of

PSE, even when controlling for such other factors as social class background, race, scholastic

ability (measured by test score), and the race and class composition of both neighborhood and

school. Each additional activity increased the odds of graduation by 40%, and PSE by nearly

31%. Only two categories did not improve graduation odds – occupational activities and student

government – while every activity (except occupational activities, as hypothesized) bettered the

chances of PSE.

As mentioned above, Hanks and Eckland (1976) found that students who planned to go to

college, routinely associated with college-oriented peers, and discussed their plans with teachers

were more likely to be involved in extracurriculars. Likewise, Otto and Alwin (1977) showed

evidence that those involved in extracurriculars had higher aspirations for both educational and

occupational attainment, and ultimately realized higher levels of attainment than non-

participants. These two works provide a basis for understanding why, even holding family

background, ability, and social context constant, participants in extracurriculars still have higher

odds of graduating high school. As a result of their participation, these students are in frequent

contact with achievement-oriented peers, likely developing higher hopes and expectations for the

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future, and can look to earlier examples of participants who have realized such hopes as support

for their own aspirations. Broh’s (2002) use of social capital as a mediator in the relationship

between extracurriculars and outcomes suggests that these aspirations grow from increasing

levels of communication between students and their parents, teachers, and peers. As Broh (2002)

demonstrates, social capital – frequency of talking with parents and teachers, parent contact with

the school and other parents – is increased by student participation in extracurriculars. These

relationships and the communication they engender are the means by which aspirations and

expectations can be passed to and taken on by students involved in school activities.

These relationships – student-to-student, student-to-parent, parent-to-parent, etc. – also

serve as a means of social control (Hirschi 1969), encouraging compliance with school norms

and expectations. This compliance is often sought explicitly, by requiring certain levels of grades

to continue participation, whether formally by school rules, or informally by parents who permit

participation only if students maintain adequate (according to the parent) levels of achievement.

These relationships also reaffirm desirable norms like completing homework, eventually

graduating, and continuing one’s education at the next level. The social support derived from

more and better relationships with peers and adult role models aids students in dealing with the

hardships of adolescence, and helps them to continue in school.

This network of relationships resulting from extracurricular participation can also serve

as a means of transmitting information, another form of social capital (Coleman 1988). This

information might include details on acquiring financial aid for college (thereby encouraging

high school completion, and facilitating PSE), tutoring services for students struggling with

classwork, help navigating college admissions processes, and so forth. All of these would

facilitate the kinds of increased aspirations mentioned above, and, through these increased

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aspirations, serve to motivate students to graduate and continue their education beyond high

school.

Contextual Effects on Educational Attainment. Other elements of the high school experience

(besides taking part in school-based activities) proved to have effects on graduation chances and

PSE attendance, most of which are at the school level. As with extracurricular participation, the

odds of graduating were more dependent on social class measures than on race measures, both of

the school and of the neighborhoods of residence. In all nine models of the odds of graduation,

the proportion of the student body on free- or reduced-price lunch had a negative effect on the

odds a student would earn a diploma. Likewise, in all but two of the graduation models the

proportion of students from single parent homes also had a significant, negative effect.3 In other

words, schools made up of poor kids (or more poor kids than the national average) have a

smaller proportion of them graduating, and the odds of a student from one of these schools

graduating continues to shrink as more and more of the student body is poor.

Furthermore, attending a private school increases the odds of graduation, while going to

an urban school decreases them. These two variables, while not direct measures of social class,

are certainly correlated with it – private schools positively, urban schools negatively – thus

supporting the same pattern of results already described. Still, it is interesting to note that, even

controlling for all of the other factors listed, attending an urban school still lessens the likelihood

of graduation, while private schoolers are more likely to graduate. This may reflect differences in

the cultural processes at work. In the private school, instruction and guidance counseling (for

example, counseling about potential occupational trajectories) are directed at PSE, and students

are oriented towards higher education. In urban schools, greater opportunities for work perhaps

not even requiring a high school degree exist, in conjunction with elements of oppositional

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culture (Farkas, Lleras, and Maczuga 2002), both of which pull students away from completing

the high school program.

None of the school racial composition variables had a significant effect on the odds of

finishing high school (and, at the individual level, only Asian students differed from white

students in their odds of graduation). Again, the results suggest that it is not race, but social class,

that is a determining factor in educational attainment. Common opinion holds that minority

students, especially those in minority-dominated schools (at least when defining minority as

black or Hispanic), generally do not value education and therefore are less involved in school

and have higher rates of dropping out. However, these results show that, when holding constant

the social class of students as well as the racial and social class environments of their

neighborhood and school, minority students have no difference from white students (or exceed

them, in the case of Asian students) in their odds of graduating high school.

The overall class level of the school a student attends has important effects on their

chances for educational attainment, and when measured at the individual level, social class is one

of the most robust predictors of educational attainment, maintaining a significant effect in nearly

all analyses made, in this study and countless others, regardless of the other factors included.

When I introduce the extracurricular variables, my analyses reveal a most intriguing and

potentially insightful finding. As shown in the tables in the previous chapter (specifically Tables

4.13 and 4.14), individual SES is indeed a strong predictor of graduating high school. In the first

three models – the exogenous factors, the base model, and the interaction model not including

extracurricular variables – SES is significant, and each increment in SES increases the odds of

graduating by 20%. But it is from this effect that the extracurricular measures seem to be

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drawing much of their explanatory power. When including the extracurricular variables, SES not

only decreases in magnitude, but falls out of significance (for p<.05) in all but one model.

This suggests that one of the ways in which higher SES actually works to produce higher

educational attainment is through involvement in the extracurriculum. Dimaggio (1982) argues

for the benefits of “high culture,” positing that going to the opera or visiting museums – more

common among higher-SES groups – helps students develop skills (verbal, written, etc.) that are

valued in schools. Farkas (1996), building on Swidler (1986), suggests it is the day-to-day habits

and styles (their cultural capital) learned by students, especially at young ages, that is largely

deterministic of their achievement and attainment. The value of extracurricular participation may

be one of those things that students of higher SES learn and practice that facilitates greater

educational success. As discussed in the section on rates of participation, students of greater SES

have many advantages, including economic resources, time, and the social support necessary, for

taking part in school-sponsored activities that those of lower SES frequently do not have. This

allows students of higher SES access to the role modeling, information, and social support that

comes with participation, and thereby facilitates their greater educational attainment.

Postsecondary attendance is also affected primarily by class measures, though racial

composition measures have a greater influence on graduation odds, at least in terms of school

racial composition. (Surprisingly, none of the neighborhood variables had a significant effect on

PSE attendance.) Attending a school with lots of students from single parent homes has a

tremendously negative effect on the odds of PSE attendance, as does attending a school with lots

of kids on free- or reduced-price lunch. Lower overall SES (and concomitant lack or resources)

prevents the development of an “attainment ethos", as discussed earlier. Likewise, attending

either an urban or a rural school makes a student less likely to attend PSE than an equivalent

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student in a suburban school (the reference category.) Suburban kids are still the most likely to

attend college. Urban kids experience more of the oppositional culture discussed above, and have

more options for making a living (legal or otherwise) by virtue of living “in the city,” while they

and rural kids may face obstacles like the need to work to support the family and a lack of

precedent for attending college.

As noted above, racial composition measures have significant effects on PSE attendance.

Schools with a larger than average proportion of Asian students have a positive effect on the

odds of PSE attendance, while a larger than average Hispanic student body has a negative effect.

This “Asian school effect” may be a result of the achievement orientation associated with Asian

students and their families “rubbing off” on other students in the school. In discussing these

effects on extracurricular participation, it was suggested that the involvement of Asian students,

and their larger proportion of the student body, may lead to the lower levels of involvement seen

among black and Hispanic students in these schools, since the activities may come to be seen as

“white and Asian activities.” However, in the case of PSE attendance, the fact that Asian

students are more likely to go on for PSE provides an example to others that “kids from this

school do go to college.” While in the day-to-day milieu of high school social worlds, kids need

to associate with a group, and these groups tend to follow racial lines (Hallinan and Williams

1989). This racial group affiliation in turn is the basis for the non-participation (or lesser

participation) of black and Hispanic kids in these schools. But once out of school and pursuing

adult roles, examples of possible life trajectories can come from many places, and what more

powerful place than a former schoolmate? Kids from a “more Asian” school probably know

more former schoolmates who attended some PSE, given that Asian students are even more

likely to attend than whites, and that we are talking about students from a school that had a larger

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than average share of Asian students. Thus, they have more examples of former classmates

pursuing higher education, and come to realize it is a path they, too, might pursue.

An unexpected result was the strong positive effect of higher-than-average rates of LEP

students on PSE attendance. This is unexpected in that language barriers would seemingly inhibit

further attainment, but the positive effect no doubt reflects the “greener pastures” perspective

associated with many immigrants and their reasons for coming to the U.S. Immigrants move to a

new country in pursuit of better opportunities, and immigrant status is associated with higher

educational expectations for the children of immigrants (Hao and Bonstead-Bruns 1998).

CONCLUSIONS

Extracurricular participation is a vital segment of the educational experiences of

America’s teenagers. It provides opportunities for expression; academic, psychological and

social skill development; integration into the school environment; and the development of

relationships that both enhance and extend the social network students would have otherwise. All

of these can translate into improved life chances, primarily by facilitating greater educational

attainment.

The results of this study show that participation in the extracurriculum is not hindered by

individual race, nor the racial context of one’s neighborhood or school. Instead, minorities are

shown to have higher levels of participation than whites, though the combination of being a

minority student in a minority neighborhood has negative effects on participation rates. A

minority student in an average neighborhood is more likely than a white student to take part, but

a minority student in a neighborhood or school of higher than average minority racial

composition is less likely than the minority student in an average neighborhood or school. The

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social class composition of the school or neighborhood is the important factor, in that economic

disadvantage precludes participation, particularly the SES level of the student body.

The extracurriculum does indeed increase both the achievement and the attainment of

students: those who participate in extracurricular activities have higher grades, increased odds of

graduating high school over non-participants, and higher odds of attending some form of

postsecondary education as well. This is not an entirely new finding, but my project makes two

important contributions to the body of literature on extracurricular involvement.

First, it has been questioned whether all extracurricular activities have positive effects on

attainment, or if it is only academic-oriented programs, or only sports, etc. This project makes

finer distinctions among types of activities, and finds that nearly all kinds of extracurriculars

have benefits to students in terms of their grades and their odds of graduation and of attending

PSE. Only Cheerleading, Fine Arts, and Occupational Clubs had no significant effects no grades.

Only Occupational Activities – those aimed primarily at employment following high school –

did not have positive effects on attainment, which is understandable given the focus these kinds

of programs have on getting students practical, vocational experience that they can put to use

right away. The other categories of activities all improve the chances a student has of graduating

and of going on to college. In an age where increasing attention and importance is being placed

on easily quantifiable outcomes like standardized test scores, these results show that there are

benefits to other areas that schools can focus on besides classroom instruction and the numbers

generated by one-size-measures-all assessments. The common adage that “most of what you

learn is outside the classroom” is given support by my findings, and lends weight to arguments in

favor of keeping school activities available to students.

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Second, and drawing on the last point above, this study reveals the commitment to

schools and school-based activities exhibited by minority students, and the benefits they derive

there from. This is important to note because so many minority students attend schools in

districts facing financial troubles, and often the first thing threatened by budget cuts is funding

for extracurriculars. In a time when so much attention is being given to ways to close the

achievement and attainment gaps between whites and minorities, it is ironic that something that

clearly benefits minority students, and something that they clearly take advantage of, is the first

thing taken away. The results of this study demonstrate the importance of keeping

extracurriculars available, particularly for minority students. Minority participation in

extracurricular activities helps alleviate dropout rates, and improve graduation and college

attendance rates, both issues of high importance among education researchers and policy makers.

Thus, the current debates surrounding the methods of funding our public schools raging in many

states can find evidence in the results attained here that it is important to “level the playing field”

(pun intended) for all public school students. By making more equitable and consistent the

funding provided to public school systems, the more equal will be the opportunities that schools

can provide to all students. One facet of such equal opportunities would be an extracurriculum

that is provided to all students, regardless of school system.

Furthermore, these results contradict common misperceptions of minorities as less

committed to and invested in school and school-based activities. Individual minority students are

often stereotyped as not valuing education, or being less interested in school. While oppositional

culture does exist in relation to academic concerns (grades, homework completion, etc.) (Farkas,

Lleras, and Maczuga 2002), in general minority students do value participation in activities

sponsored by their school, and not just athletics as is sometimes further assumed. Their

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participation reveals a commitment to their school that is often not acknowledged or recognized,

or is overshadowed by lower academic performance. Concerns about minority academic

performance and attainment need to be informed by these results, and support for maintaining

the availability of extracurricular activities can be drawn from the findings of this study.

REFERENCES


Coleman, James S. 1961. The adolescent society: The social life of the teenager and its impact on education. New York: Free Press.

—. 1988. "Social capital in the creation of human capital." American Journal of Sociology 94:S94-S120.

Dimaggio, Paul. 1982. "Cultural capital and school success: The impact of status culture participation on the grades of U.S. high school students." American Sociological Review 47:189-201.


Farkas, George. 1996. Human capital or cultural capital? Ethnicity and poverty groups in an urban school district. New York: Walter de Gruter.


Hallinan, Maureen T. and Richard A. Williams. 1989. "Interracial Friendship Choices in Secondary Schools." American Sociological Review 54:67-78.


Hao, Lingxin and Melissa Bonstead-Bruns. 1998. "Parent-Child Differences in Educational Expectations and the Academic Achievement of Immigrant and Native Students." Sociology of Education 71:175-198.

Hirschi, T. 1969. Causes of delinquency. Berkeley: University of California Press. Kao, Grace. 1995. "Asian Americans as Model Minorities? A Look at Their Academic

Performance." American Journal of Education 103:121-159. Macleod, Jay. 1987. Ain't No Makin' It: Aspirations and Attainment in a Low-Income

Neighborhood. Boulder, CO: Westview Press. Ogbu, John U. 1990. "Minority Education in Comparative Perspective." The Journal of Negro

Education 59:45-57. Otto, L.B. and D.F. Alwin. 1977. "Athletics, Aspirations, and Attainments." Sociology of

Education 50:102-113. Philips, Susan U. 1976. "Access to Power and Maintenance of Ethnic Identity as Goals of

Multicultural Education: Are They Compatible?" Anthropology & Education Quarterly 7:30-32.

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Shouse, Roger. 2004. "Democratic spirit and moral learning in comprehensive high schools." in The Comprehensive High School Today, edited by F. M. Hammack. New York: Teachers College Press.

Swidler, Anne. 1986. "Culture in action: Symbols and Strategies." American Sociological Review 51:273-286.

Tsang, Sau-Lim. 1984. "The Mathematics Education of Asian Americans." Journal for Research in Mathematics Education 15:114-122.


Wong, Paul, Chienping Faith Lai, Richard Nagasawa, and Tieming Lin. 1998. "Asian Americans as a Model Minority: Self-Perceptions and Perceptions by Other Racial Groups." Sociological Perspectives 41:95-118.

1 There is an argument for cultural differences in the norms of such neighborhoods that would persist regardless of SES level of the area, which I make later in the following paragraph. 2 The third is basketball, which, if analyzed separately, would likely show that students in urban schools are more likely to participate. 3 In five of the nine, the effect was significant at p< .05 or better; in two of the models the effect was significant at p <.10, and in the remaining two, the effect approached the p <.10 level, with p-values of .15 and .12.

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Jason M. Smith Assistant Professor of Sociology

The University of Alabama in Huntsville Morton Hall

Huntsville, AL 35899 (256) 824-6190

Ph.D., The Pennsylvania State University – August, 2006 Department of Sociology: Dual Degree Program in Sociology and Demography Certificate in Quantitative Methodology Dissertation: Neighborhood & School Context, Extracurricular Participation, & Educational Attainment Committee: Drs. George Farkas (Chair), Suet-Ling Pong, Barry Lee, and Roger Shouse

M.S., Miami University – 2000 Department of Physical Education, Health, and Sport Studies: Sport Organization Program Thesis: Professional Sport, Urban Redevelopment, and Community Response: The Case of Cincinnati Committee: Drs. Alan Ingham (Chair), Mary McDonald, and Thelma Horn

B.S. Miami University – 1995 Department of Teacher Education: Secondary Social Studies Program; Advisor: Dr. Michael Fuller

Publications Smith, Jason M. (Forthcoming). “Between the Lines, On the Stage, and In the Club: Additional Ways

Students Find to Overcome Disadvantage through School.” In Child Poverty in America Today Vol. 4, The Promise of Education, edited by Barbara A. Arrighi and David J. Maume. Westport, CT: Praeger Publishers.

Smith, Jason M., & Alan G. Ingham. (2003). On the Waterfront: Retrospectives on the Relationship between Sport and Communities. Sociology of Sport Journal, 20, 252-274.

Professional Presentations Smith, Jason M. 2004. Social Capital and Fertility: Testing Coleman's Closure Hypothesis. American

Sociological Association. San Francisco, CA.

Smith, Jason M. 2003. Extracurricular Participation and Educational Attainment: A Gateway to Opportunity? American Sociological Association. Atlanta, GA.

Smith, Jason M. & Alan G. Ingham. 2000. From the Bottom Up: Public Subsidy for Stadia – The Case of Cincinnati. Sport in the City 2000. Indianapolis, IN.

Smith, Jason M. 1999. The Insider-Outsider Debate: What is it, and What Dilemmas Does it Pose for Qualitative Research? North American Society for the Sociology of Sport. Cleveland, OH

Invited Presentations Smith, Jason M. 2006. Classroom Management Extended: Issues for School Transportation. Fullington Bus

Lines, and Clearfield Area School District. Clearfield, PA.

REFERENCES NAME: George Farkas Barry Lee Glenn Firebaugh Stephen Matthews PHONE: (814) 865-6428 (814) 863-7430 (814) 865-0172 (814) 863-9721 EMAIL: [email protected] [email protected] [email protected] [email protected]

mailto:[email protected]

neighborhood and school context, extracurricular

Documents