SOCIOLOGY EDUCATION - American Sociological Association

SOCIOLOGY 

OF 

EDUCATION 

Volume 80 July 2007 Number 3 

Contextual Explanations of School Choice 

DOUGLAS LEE LAUEN 

Staying Back and Dropping Out: 

The Relationship Between 

Grade Retention and School Dropout 

ELIZABETH STEARNS, STEPHANIE MOLLER, 

JUDITH BLAU, AND STEPHANIE POTOCHNICK 

Gender, Obesity, and Education 

ROBERT CROSNOE 

Making It Through the First Year of College: 

The Role of Students’ Economic Resources, 

Employment, and Living Arrangements 

ROBERT BOZICK 

A Journal of the American Sociological Association

Hanna Ayalon 

Tel Aviv University 

Pamela R. Bennett 

Johns Hopkins University 

William J. Carbonaro 

University of Notre Dame 

Wade M. Cole 

Washington State Institute for 

Public Policy 

Donna J. Eder 

Indiana University 

Cynthia Feliciano 

University of California–Irvine 

Patricia C. Gandara 

University of California–Davis 

Sara Goldrick-Rab 

University of Wisconsin-Madison 

Joseph C. Hermanowicz 

University of Georgia 

EDITOR 

Barbara Schneider, Michigan State University 

DEPUTY EDITOR 

John Robert Warren, University of Minnesota 

Charles Hirschman 

University of Washington 

Erin McNamara Horvat 

Temple University 

Sylvia Hurtado 

UCLA 

Douglas Lee Lauen 

University of North Carolina- 

Chapel Hill 

Samuel R. Lucas 

University of California–Berkeley 

Daniel A. McFarland 

Stanford University 

Stephen Morgan 

Cornell University 

Kelly Raley 

University of Texas 

Sean F. Reardon 

Stanford University 

MANAGING EDITOR 

Wendy Almeleh 

EDITORIAL ASSISTANT 

Michelle Llosa 

EXECUTIVE OFFICER 

Sally T. Hillsman 

Xue Lan Rong 

University of North 

Carolina–Chapel Hill 

Salvatore Saporito 

College of William and Mary 

Kathryn Schiller 

SUNY Albany 

Kathleen M. Shaw 

Temple University 

Christopher B. Swanson 

Editorial Projects in Education 

William T. Trent 

University of Illinois, Urban- 

Champagin 

Karolyn Tyson 

University of North Carolina- 

Chapel Hill 

Julia Wrigley 

CUNY Graduate Center 

MISSION STATEMENT: The journal provides a forum for studies in sociology of education and human social development 

throughout the life cycle. It publishes research from all methodologies that examines how social institutions and 

individuals’ experiences in these institutions affect educational processes and social development. Such research may 

span various levels of analysis, from the individual to the structure of relations among social and educational institutions, 

and may encompass all stages and types of education at the individual, institutional, and organizational levels. 

SOCIOLOGY OF EDUCATION (ISSN 0038-0407) is published quarterly in January, April, July, and October by the 

American Sociological Association, 1307 New York Avenue, Suite 700, Washington, DC 20005-4701, and is printed 

by Boyd Printing Company, Albany, New York. Periodicals postage is paid at Washington, DC, and additional mailing 

offices. POSTMASTER: Send address changes to Sociology of Education, 1307 New York Avenue, Suite 700, 

Washington, DC 20005-4701. 

Address manuscripts and communications for the editors to Barbara Schneider, Editor, SOCIOLOGY OF EDUCATION, 

Department of Education, Michigan State University, 516B Erickson Hall, East Lansing, MI 48824; e-mail 

soe@msu.edu. 

Send advertisements, changes of address, and subscriptions to the Executive Office, American Sociological Association, 

1307 New York Avenue, Suite 700, Washington, DC 20005-4701. Subscription rates for members, $35 ($25 student 

members); institutions, $164. Rates include postage in the United States and Canada; elsewhere, add $20 per journal 

subscription for international postage. Single issues available: $7 to members and $20 to institutions. New subscriptions 

and renewals will be entered on a calendar-year basis only. Change of address: Send old and new addresses to the ASA 

Executive Office six weeks in advance. Claims for undelivered copies must be made within the month following the regular 

month of publication. The publishers will supply missing copies when losses have been sustained in transit and the 

reserve stock will permit. 

Copyright ©2007, American Sociological Association. Copying beyond fair use: Copies of articles in this journal may be 

made for teaching and research purposes free of charge and without obtaining permission, as permitted under Sections 

107 and 108 of the U.S. Copyright Law. For all other purposes, permission must be obtained from the publisher. 

The American Sociological Association acknowledges, with appreciation, the facilities and assistance provided by 

Michigan State University.

SOCIOLOGY 

OF 

EDUCATION 

Volume 80 July 2007 Number 3 

Contents 

Contextual Explanations of School Choice 

DOUGLAS LEE LAUEN 179 

Staying Back and Dropping Out: The Relationship Between 

Grade Retention and School Dropout 

ELIZABETH STEARNS, STEPHANIE MOLLER, JUDITH BLAU, AND 

STEPHANIE POTOCHNICK 210 


ROBERT CROSNOE 241 

Making It Through the First Year of College: The Role of Students’ 

Economic Resources, Employment, and Living Arrangements 

ROBERT BOZICK 261

NOTICE TO CONTRIBUTORS 

Editorial Procedures 

All papers considered appropriate for this journal are reviewed anonymously. To ensure anonymity, authors’ 

names, institutional affiliations, and other identifying material should be placed on the title page only. Papers 

are accepted for publication subject to nonsubstantive, stylistic editing. A copy of the edited paper is sent to 

the author for final review. Proofs of articles are sent only to authors who reside in the United States. 

Submission of a paper to a professional journal is considered an indication of the author’s commitment to 

publish in that journal. A paper submitted to this journal while it is under review for another journal will not 

be accepted for review. 

Preparation and Submission of Manuscripts. 

1. Type all copy (including indented material, references, and footnotes) double-spaced in no smaller than 

12-point type using 11/2-inch margins on all sides. 

2. Type each table on a separate page. Insert a note in the text indicating where the table should appear. 

3. On an article’s acceptance, submit camera-ready art for all figures, rendered on a laser printer or as 

glossy prints or electronic format. 

4. Include an abstract of no more than 100 words. 

5. Submit four copies of the paper and retain the original for your files. Enclose a stamped, self-addressed 

postcard so we can acknowledge receipt of your paper. 

6. A check or money order for $25.00, payable to the American Sociological Association, must accompany 

each submission. This fee is waived for papers written by student members of the ASA. The submission 

fee reflects a policy of the ASA Council and Committee on Publications, which affects all ASA 

journals. It is a reluctant response to the accelerating costs of manuscript processing. 

Reference Format 

1. In the text: All references to books, articles, and other works should be identified at the appropriate point 

in the text by the surname of the author and year of publication; add page numbers only when citing 

statistics or direct quotes. Endnotes should be used only for substantive observations and explanations. 

Subsequent citations of a source should be identified in the same way; do not use “ibid.,” “op. cit.,” or 

“loc. cit.” 

a. If the author’s name is part of the narrative, place only the year of publication in parentheses: 

Duncan (1959). Otherwise, place both the name and the year, with no intervening punctuation, 

in parentheses: (Duncan 1959). 

b. Insert page numbers, preceded by a colon after the year of publication: (Kuhn 1970:120–45). 

c. If the work cited has three or fewer authors, list all authors in the first citation; thereafter, include 

only the name of the first author followed by “et al.” If the work has four or more authors, include 

only the name of the first author followed by “et al.” in all citations. 

d. Abbreviate or shorten the names of institutional or corporate authors, making sure that the text 

citation and the entry in the reference list begin with the same element. 

e. Distinguish two or more works by the same author with the same publication date by appending 

letters (a, b, c) to the date: (Levy 1965a). 

f. Separate a series of references with semicolons and enclose them in a single pair of parentheses: 

(Featherman and Hauser 1979; Coleman et al. 1982; U.S. Bureau of Census 1981). 

2. In the Reference List: List all entries alphabetically by author and, within author, by year of publication. 

List all authors in citations of multiauthor works; do not use “et al.” in the reference list. 

Examples follow: 

Bourdieu, Pierre, 1977. “Cultural Reproduction and Social Reproduction.” Pp. 487–511 in Power and Ideology 

in Education, edited by J. Karabel and A.H. Halsey. New York: Oxford University Press. 

Coleman, James S., Thomas Hoffer, and Sally B. Kilgore. 1982a. “Cognitive Outcomes in Public and Private 

Schools.” Sociology of Education 55:65–76. 

——. 1982b. High School Achievement: Public, Catholic and Other Private Schools Compared. New York: Basic. 

Mare, Robert D. 1979. “Change and Stability in Educational Stratification.” Paper presented at the annual 

meeting of the American Sociological Association, Boston. 

Marx, Karl (1867) 1976. Capital. Vol. 1. Translated by S. Moore and E. Aveling. New York: International. 

U.S. Bureau of the Census. 1979. 1970 Census Population and Housing. Fourth Count Population Summary 

Tape. Machine-readable data file. Washington, DC: U.S. Bureau of the Census (producer). Rosslyn, VA: 

DUALabs (distributor).


Robert Crosnoe 

University of Texas at Austin 

Obesity is a health condition, but its consequences extend far beyond the realm of health. To 

illuminate an important route by which the experience of obesity can filter into the status 

attainment process, this study drew on nationally representative data from the National 

Longitudinal Study of Adolescent Health to test a social psychological model of the gendered 

link between obesity and education. Obese girls were less likely to enter college after high 

school than were their nonobese peers, especially when they attended schools in which obesity 

was relatively uncommon. Additional analyses revealed that increasing rates of internalizing 

symptoms, self-medication, and academic disengagement explained about one-third of 

the obese girls’ lower odds of college enrollment. Obese boys, on the other hand, did not differ 

from their peers—no matter what their school context—in college enrollment. 

Obesity gets a lot of press these days. 

Almost daily, news stories and governmental 

reports trumpet alarming 

statistics about the pandemic of obesity. For 

the most part, the precipitous growth of the 

collective American waistline is cast as a public 

health issue, but its significance goes 

beyond physical health (Anderson and 

Butcher 2006; Ferraro and Kelley-Moore 

2003). For example, some evidence suggests 

that obesity is related to lower academic 

achievement, educational attainment, and 

earnings. Such evidence points to the status 

attainment process as a valuable crucible in 

which to assess the societal implications of rising 

obesity rates (Ball, Crawford, and Kenardy 

2004; Canning and Mayer 1967; Crosnoe 

and Muller 2004; Pagan and Davila 1997). 

To explore the implications of obesity for 

the status attainment process, the study presented 

here investigated whether obesity during 

secondary school is associated with college 

matriculation after this period among 

young people in the National Longitudinal 

Study of Adolescent Health (Add Health). 

Three questions guided the investigation: (1) 

Why does obesity affect college enrollment? 

(2) in what contexts are these effects most 

common? and (3) for whom are these effects 

most common? No consistent evidence links 

obesity to cognitive skills or scholastic abilities, 

but its marked devaluation in American 

culture suggests that the answers to these 

questions lie in the social psychology of obesity 

(Carr and Friedman 2006; Cawley 2001; 

Crandall 1994). Working from key sociological 

traditions, therefore, I posit that the social 

stigma of obesity triggers psychological and 

behavioral responses that interfere with college 

matriculation, especially in contexts (in 

this case, schools) and in segments of the 

youth population (in this case, girls) in which 

the stigma of obesity is most likely to be felt. 

BACKGROUND 

Determining why, in what contexts, and for 

whom obesity is related to college enrollment 

is an important enterprise. First, educational 

attainment is central to the life course 

and the economy (Kerckhoff 1993). Second, 

the gendered nature of the effects of obesity 

Sociology of Education 2007, Vol. 80 (July): 241–260 241

242 Crosnoe 

may contribute to larger patterns of gender 

inequality in socioeconomic status as obesity 

rates skyrocket (Conley and Glauber 2005). 

Third, research of this kind informs the sociological 

understanding of the connection 

between education and health by elucidating 

how health risks filter into the educational 

process in ways that compound these risks 

(Puhl and Brownell 2003). 

Obesity and Status Attainment 

According to medical standards, obesity is the 

highest end of the body mass index (BMI), a 

ratio of weight to height. Recently, the prevalence 

of obesity has increased steadily, including 

among children and youths. For the most 

part, this trend has sparked widespread concern 

because of fears that it forecasts a major 

public health crisis (Anderson and Butcher 

2006; Campos et al. 2006; Freedman et al. 

1999; National Institutes of Health 2003). 

Although important, the relevance of early 

obesity to lifelong health is not the only reason 

to be concerned about rising obesity 

rates. For example, several studies have 

reported that obese students often achieve 

lower grades than do their peers (see, e.g., 

Crosnoe and Muller 2004; Sobol and Dietz 

1997). Others have documented the negative 

effects of obesity on multiple aspects of work, 

including earnings and promotions (Ball et al. 

2004; Conley and Glauber 2005; Loh 1993; 

Pagan and Davila 1997). Thus, obesity can 

disrupt long-term status attainment. These 

nonhealth risks of obesity are arguably just as 

important to understand because they can 

produce economic inequality and exacerbate 

health inequality between obese and 

nonobese individuals. 

Assessing the effects of obesity on key 

points in the status attainment trajectory, 

therefore, is valuable. In a modern economy 

with an expanding service-information sector 

and a shrinking manufacturing sector, postsecondary 

credentials are crucial for obtaining 

stable, well-paying jobs. Thus, enrolling in 

college after high school is the first step in 

pushing through the bottleneck in this new 

hourglass-shaped economy (Hirschman 

2001; Schneider and Stevenson 1999). If 

obese youths are less able than their 

nonobese peers to enroll immediately in college, 

then they are at an early disadvantage 

and must play catch-up. Consequently, this 

transition from high school to college is a 

valuable window for evaluating the divergence 

in socioeconomic trajectories related to 

obesity. 

Linking Obesity to Educational 

Attainment 

Canning and Mayer (1966, 1967) established 

such a link between obesity and college 

enrollment in a community sample, attributing 

it primarily to biases among college 

admissions officers. Because much has 

changed in the intervening decades, including 

increasing rates of college attendance and 

the aforementioned rise in obesity, investigating 

and unpacking the risks of obesity for college 

enrollment is still timely. If, as expected, 

these risks persist today, then the challenge is 

to understand why. Two influential sociological 

models—Cooley’s ([1902] 1983) lookingglass 

self and Goffman’s (1963) social stigma 

model—are relevant to answering this “why” 

question. Neither model had obesity or education 

explicitly in mind, but both demonstrated 

the value of using social psychological 

insights to understand the educational implications 

of the obesity pandemic. 

To review, the looking-glass self contends 

that humans rely on messages they receive in 

social interaction to define and evaluate their 

general worth (Cooley [1902] 1983). 

Originally, this model highlighted internalization, 

or the tendency to incorporate repeated, 

consistent social feedback into the selfconcept 

(e.g., “Everyone says I am bad, so I 

must be bad” and “People treat me nice, so I 

must be appealing”). More recent extensions 

have emphasized externalization (Cast, Stets, 

and Burke 1999; Yeung and Martin, 2003). 

For example, individuals may engage in a 

variety of agentic behaviors to alter, avoid, or 

dismiss feedback that could potentially damage 

their senses of self, such as avoiding situations 

in which negative feedback is likely or 

trying to impress potential critics. 

Goffman’s (1963) social stigma model 

focuses explicitly on negative social feedback 

that is elicited by a general category of phys-

Gender, Obesity, and Education 243 

ical, behavioral, or demographic characteristics 

that carry some widely agreed-upon low 

status. Individuals with these stigmatized 

traits will then be vulnerable to receiving negative 

feedback that is real (e.g., harsh remarks 

or shunning), implied (e.g., overpoliteness), 

or imagined (e.g., perceiving constant judgment 

from others). This general cloud of negative 

feedback that follows a person in a stigmatized 

category can be internalized or 

externalized in much the same way as the 

more direct, interactive feedback highlighted 

by the looking-glass self (Link and Phelan 

2001; for specific applications of models of 

stigma to obesity, see Cahnman 1968; 

Dejong 1980). 

Obesity, Psychosocial Responses, 

and College Enrollment 

Integrating the basic insights of these two traditions 

and connecting them to empirical evidence 

from educational research identifies 

possible pathways between obesity in adolescence 

and college enrollment in young adulthood. 

The driving force is the social meaning 

of obesity. Americans tend to view obese individuals 

in extremely negative terms—as unattractive, 

ugly, lazy, and dumb (Allon 1981; 

Dejong 1980). Despite the recent upswing of 

obesity, these views have not changed a great 

deal and, what is surprising, are equally 

strong among those who are overweight 

themselves (Goodman and Whitaker 2002; 

Latner, Stunkard, and Wilson 2005; Puhl and 

Brownell 2003; Quinn and Crocker 1999). 

Although the stigma attached to obesity 

varies by race and social class, it is, for the 

most part, highly visible in the general culture, 

including the media (Adams et al. 2000; 

Crandall 1994; Harrison 2001; Puhl and 

Brownell 2003). Thus, in the spirit of 

Goffman, obesity clearly is a socially stigmatized 

trait. 

As a stigmatized trait, obesity generates 

negative feedback in adolescence. This feedback 

may occur directly in social situations, as 

in the well-documented tendency for young 

people to bully, isolate, and ostracize overweight 

peers (Halpern et al. 2005; Janssen et 

al. 2004; Strauss and Pollack 2003). 

Alternatively, it may arise more subtly from 

social comparison, as in the equally well-documented 

tendency for overweight youths to 

fear that they do not measure up to culturally 

defined physical ideals (Ewell et al. 1996; 

Harrison 2001; Latner et al. 2005). Two general 

responses to either form of social feedback 

are likely. First, obese youths often internalize 

social feedback, with negative feedback 

leading to more negative self-concepts (Ge et 

al. 2001; Needham and Crosnoe 2005). 

Second, given the tendency for adolescents 

to self-medicate pain and loneliness and to 

disidentify with contexts in which they feel at 

risk (Aseltine and Gore 2000; Dance 2002), 

obese youths may externalize negative feedback 

by taking drugs, drinking, or disengaging 

from school. 

According to the stigma model, the looking-glass 

self, and recent elaborations of both 

models, responses to stigma are often shortterm 

acts of self-preservation that ultimately 

prove disastrous. Similarly, responses to the 

stigma of obesity may be incompatible with 

educational progress. Internalization disables 

the confidence and motivation that are needed 

to set the goals and take up the challenges 

that are integral to the pursuit of a postsecondary 

education (Wigfield and Eccles 2002). 

One form of externalization, self-medication, 

distracts from schoolwork and introduces 

conflict into the relationships with parents 

and teachers that support educational attainment 

(Crosnoe 2006). A second form of 

externalization, disengagement from school, 

disrupts the accumulation of academic credentials 

(e.g., grades and course requirements) 

that are needed to pursue a postsecondary 

education (Smerdon 1999). In short, 

internalizing and externalizing responses to 

the stigma of obesity set the stage for truncated 

trajectories of educational attainment 

in the long run. 

Of course, these psychosocial and educational 

consequences of the stigma of obesity 

are situated in a host of other life-course 

experiences. For example, obesity, psychosocial 

adjustment, and college enrollment are 

all reciprocally related to each other and cooccur 

with other factors, including academic 

achievement, the location of the school, and 

participation in activities (Anderson and 

Butcher 2006; Carr and Friedman 2005;

244 Crosnoe 

Goodman and Whitaker 2002). As another 

example, stigmatized traits can also trigger 

positive responses, as illustrated by the tendency 

for a minority of obese youths to 

become academic stars as a balance to their 

physical stigma (Crosnoe and Muller 2004; 

Yeung and Martin 2003). Finally, different 

demographic groups maintain different standards 

of physical appearance. Ample evidence 

suggests that these standards are more 

forgiving among racial minorities, especially 

African Americans, than among whites, a difference 

that is largely a function of systematic 

racial differences in socioeconomic status 

(Crandall 1994; Halpern et al. 2005; Latner et 

al. 2005). Thus, effectively connecting obesity 

to psychosocial responses to college enrollment 

requires the careful consideration of 

confounding factors, feedback loops, adaptive 

responses, and racial/class variation. 

The Importance of Context 

Having addressed the “why?” question, the 

next goal is to ask: In what settings is this link 

between obesity and college enrollment most 

likely to occur? According to the classic theories 

underlying this study, local contexts can 

reinforce or counter negative social messages 

about some trait that is stigmatized in the 

general context of American society (Goffman 

1963). In other words, obesity may pose a 

social risk for most, but not all, Americans, 

and who is or is not at risk is far from random 

(Cahnman 1968; Crandall 1994; Dejong 

1980). 

This tension between local and general 

contexts is especially important in regard to 

young people. Although a mass youth culture 

exists in the United States (see Greenberg, 

Brown, and Buerkel-Rothfuss 1993), the 

valence and intensity of cultural messages 

vary considerably across specific pockets of 

this mass culture. As a bounded, identifiable 

setting of adolescent life, the school is an 

appropriate unit for considering local contexts 

of youth culture. Indeed, schools develop 

complex systems of norms and values, 

including those related to appearance (Eder, 

Evans, and Parker 1995). If two schools differ 

in their cultural assessments of weight, then 

the consequences of obesity will likely differ 

just as sharply between these two schools. 

Following my earlier work with Chandra 

Muller (see Crosnoe and Muller 2004), the 

representation of obesity in any given 

school—what students in that school tend to 

look like—is a proxy for the stigma of obesity 

in that school. If obesity makes a student 

decidedly stand out, then the intensified stigma 

of obesity in that school will compound 

the general psychosocial and subsequent 

educational risks of obesity. If obesity allows a 

student to fit in, then these general risks will 

not be as pronounced. 

Pathways between obesity and college 

enrollment and their variation across schools 

are depicted in Figure 1. Obesity triggers 

internalizing and externalizing responses in 

adolescence (A1) that, in turn, disrupt the 

transition to college after high school (A2), 

net of a host of confounding demographic, 

personal, and social factors, including race 

and socioeconomic status. This general phenomenon 

is likely to be conditioned by differences 

in the local cultures of schools, with 

obesity less likely to trigger the problematic 

responses that are so consequential for college 

enrollment in schools in which obesity is 

more common and accepted (B). 1 

The Role of Gender 

Having sketched out a conceptual model 

hypothesizing answers to the “why?” and “in 

what context?” questions, the next step is to 

ask: For whom is this model most likely to 

hold? Because the main factors in the model 

are all highly gendered, comparing adolescent 

girls and adolescent boys is an important 

tool for answering this “for whom?” question. 

First, the internalization and externalization 

of the stigma of obesity (Path A1 in 

Figure 1) will be more pronounced for girls 

than for boys. Ample evidence indicates that 

norms about weight—what is considered 

“good” or “bad” weight—are stricter and 

more publicly enforced for girls (Martin 1996; 

Wardle, Waller, and Jarvis 2002). It is not surprising 

that obesity appears to have much 

stronger, more negative, effects on girls, making 

them more likely to be emotionally distressed, 

socially isolated, and concerned 

about their appearance (Ge et al. 2001;


Figure 1. A Social Psychological Model of the Educational Risks of Adolescent Obesity

246 Crosnoe 

Halpern et al. 2005; Needham and Crosnoe 

2005). Even though such gender differences 

are less sharp in some races or social classes, 

the stakes of obesity are typically higher for 

girls, in general. Thus, although girls may be 

less likely than boys to take part in things like 

substance use or academic disengagement in 

general, they are probably more likely to do 

so in response to obesity. 

Second, the power of school context to 

condition the internalization and externalization 

of the stigma of obesity (Path B in Figure 

1) will also be stronger for girls for many of 

the same reasons. If girls are generally more 

sensitive to norms about weight, then the 

adjustment of obese girls should be more 

affected by how these girls measure up to 

local norms about weight, just as it is more 

affected by general norms. At the same time, 

a good deal of developmental research has 

demonstrated that girls are more ingrained in 

and reactive to social networks, especially 

their personal relationships, and are more 

likely to use feedback from significant others 

when they evaluate themselves (Giordano 

2003; Martin 1996). Thus, being out of step 

with schoolmates in terms of weight will have 

a greater impact on girls. 

If this conceptual model is a better fit for 

girls than for boys, then the psychosocial 

costs of obesity will be more important to 

understanding obesity-related educational 

disparities in the female population. More 

generally, these costs will be another disadvantage, 

along with discrimination and other 

powerful forces, that women face in the status 

attainment process. 

METHODS 

Data 

To test the conceptual model in Figure 1, I 

drew on nationally representative data from 

the National Longitudinal Study of 

Adolescent Health (Add Health), an ongoing 

study of American adolescents in Grades 

7–12 that began in 1994 (Bearman, Jones, 

and Udry 1997). With a multistage, stratified 

design, Add Health selected 80 schools from 

a comprehensive list of American high 

schools on the basis of their region, urbanicity, 

sector, racial composition, and size. Each 

selected school was then matched to one of 

its feeder schools, typically a middle school, 

with the probability of the feeder school 

being selected proportional to its contribution 

to the high school’s student body. The 

final sample included 132 schools. Nearly all 

the students in each school (approximately 

90,000) completed a limited paper-and-pencil 

questionnaire, the In-School Survey, in the 

1994–95 school year. These students became 

the sampling frame for three in-depth inhome 

interviews. A representative subsample 

of the In-School sample, selected evenly 

across high schools and pairs of feeder 

schools, participated in Wave I in 1995 (n = 

20,475), Wave II in 1996 (n = 14,736), and 

Wave III in 2002 (n = 15,197). The Wave II 

sample excluded all Wave I seniors. The Wave 

III sample, however, attempted to bring these 

Wave I seniors back in. 

The analytic sample for the study included 

all adolescents who participated in Waves I–III 

and had valid sampling weights (n = 10,829 

adolescents in 128 schools). White girls with 

more educated parents were slightly overrepresented 

in this analytic sample compared to 

the original Wave I sample, but the obesity 

rate was the same. This bias must be remembered 

in the interpretation of the results. Still, 

these selection filters were necessary—the 

longitudinal filter to establish the proper temporal 

ordering of variables in the theoretical 

model and the weighting filter to adjust for 

the complex sampling frame of Add Health 

(Chantala and Tabor 1999). 

Measures 

Table 1 presents descriptive statistics for all 

the study variables. Because of the centrality 

of gender to this study, these statistics are 

presented for the full sample and then for 

each gender. All significant gender differences, 

tested with a one-way analysis of variance 

(ANOVA), have been noted. 

College Enrollment Wave III responses to 

questions about schooling were collapsed 

into a binary measure of college enrollment. 

In this measure, young people who were


Table 1. Descriptive Statistics for the Study Variables, for the Full Sample and by Gender (n = 10,829) 

Full Sample Boys Girls 

M (SD) % M (SD) % M (SD) % 

Adolescent Obesity 

Wave I obesity (1/0) — 11.33 — 13.62*** — 9.29 

Adolescent/Young Adult Outcomes 

Wave I self-rejection (1/0) — 9.56 — 5.31*** — 13.34 

Wave II self-rejection (1/0) — 7.54 — 3.96*** — 10.72 

Wave I suicidal ideation (1/0) — 13.18 — 13.18*** — 16.36 

Wave II suicidal ideation (1/0) — 10.87 — 10.87*** — 13.27 

Wave I alcohol use (0–6) 1.00 — 1.07*** — .93 — 

(1.41) (1.50) (1.31) 

Wave II alcohol use (0–6) 1.08 — 1.19*** — .99 — 

(1.51) (1.62) (1.39) 

Wave I marijuana use (0–6) .90 — 1.00*** — .81 — 

(1.89) (2.00) (1.79) 

Wave II marijuana use (0–6) 1.00 — 1.09*** — .91 — 

(2.07) (2.19) (1.95) 

Wave I class failures (0–4) .48 — .56*** — .40 — 

(.84) (.90) (.77) 

Wave II class failures (0–4) .37 — .44*** — .32 — 

(.74) (.79) (.68) 

Wave I truancy (0–4) .60 — .66*** — .55 — 

(1.16) (1.21) (1.12) 

Wave II truancy (0–4) 1.94 — 2.14*** — 1.76 — 

(3.45) (3.59) (3.32) 

Wave III college enrollment (1/0) — 32.99 — 29.84*** — 35.80 

School Factors 

Proportion of obese girlsa .05 — .04** — .05 — 

(.02) (.02) (.02) 

Proportion of obese boys .06 — .06** — .06 — 

(.03) (.02) (.03)

248 Crosnoe 

Table 1. Continued 

Full Sample Boys Girls 

M (SD) % M (SD) % M (SD) % 

Selection Factors 

Gender (1 = female) — 52.90 — — — — 

Grade level (7–11) 9.33 — 9.37** — 9.29 — 

(1.48) (1.47) (1.48) 

Non-Latino/Latina white — 53.01 — 52.96 — 53.05 

African American — 20.99 — 19.33*** — 22.48 

Latino/Latina — 16.01 — 16.80* — 15.31 

Asian American — 7.25 — 8.04** — 6.55 

Other race/ethnicity — 2.68 — 2.82 — 2.55 

Family structure (1 = two parent) — 55.56 — 56.60* — 54.63 

Parental education (1–5) 2.97 —- 3.01*** — 2.93 — 

(1.25) (1.24) (1.25) 

School level (1 = middle) — 25.09 — 23.68** — 26.35 

School sector (1 = private) — 7.68 — 7.81 — 7.58 

School minority representation (0–100) 48.75 — 48.32 — 49.12 — 

(32.92) (33.10) (32.76) 

School proportion college-educated parents .34 — .34 — .34 — 

(.15) (.15) (.15) 

Measured ability (0–100) 50.21 — 52.25*** — 48.21*** — 

(27.29) (27.93) (27.54) 

Athletic status (1 = athlete) — 42.21 — 46.98*** — 37.96 

Number of friends (0–10) 3.08 — 3.09 — 3.06 — 

(2.62) (2.63) (2.62) 

Involvement with friends (0–4) 2.11 — 2.13* — 2.08 — 

(1.06) (1.10) (1.03) 

Romantic involvement (1/0) — 52.21 — 50.61** — 53.64 

*** Descriptive statistic differs significantly (p < .001) by gender, as determined by a one-way ANOVA; ** p < .01, * p < .05. 

a The first school obesity variable represents the number of obese girls in the school divided by the total number of girls and boys in the school. The 

second represents the number of obese boys in the school divided by the total number of girls and boys in the school.


currently attending or who had graduated 

from a four-year institution of higher education 

received a 1, and those who did not 

meet these two criteria (including those who 

had never attended such institutions, those 

who had dropped out of such institutions, 

and those who were enrolled in or had graduated 

from two-year institutions) received a 

0. 

Obesity Adolescents’ reports of weight and 

height at Wave I allowed the calculation of BMI 

with the formula: [weight (kg) / [height (m)] 2. 

BMI was then compared to weight x age x gender 

tables to identify adolescents who were at 

or above the 95th percentile of BMI for their 

age-gender group, the obesity threshold set by 

the Centers for Disease Control and Prevention 

(CDC 2002). Three issues with this measurement 

need to be discussed. First, self-reported 

weight and height can be inaccurate for several 

reasons. Add Health included interviewermeasured 

weight and height at Wave II. The 

correlation between interviewer-measured and 

self-reported weight is .95 at Wave II, indicating 

good reliability in the self-reported Wave I measure 

used here (Goodman, Hinden, and 

Khandelwal 2000). Second, the CDC actually 

recommends the term overweight—rather than 

obesity—for the 95th percentile and above in 

young populations, precisely because of the 

stigma attached to the term obesity. Because of 

the focus on the transition to young adulthood 

and the greater prevalence of the term obesity 

in nonhealth research, however, this article 

refers to this category as obese. Third, removing 

the girls who were recently pregnant from 

the sample and, therefore, had elevated BMIs 

did not alter the results. 

Internalization Two psychological factors 

tapped internalization. The adolescents rated 

how well they liked themselves just the way 

they were. This item was dichotomized (1 = 

disagree, 0 = neutral or agree) to measure 

self-rejection. For suicidal ideation, the adolescents 

reported whether they had seriously 

considered committing suicide in the past 

year (1 = yes, 0 = no). Wave I and Wave II versions 

of both were created to serve as predictors 

and outcomes, respectively. 

Externalization Two substance-use factors 

tapped self-medication. On the basis of prior 

Add Health research (Crosnoe 2006; Resnick 

et al. 1997), the adolescents’ responses to a 

series of items about the timing and frequency 

of different forms of substance use were 

collapsed into two six-point scales (0 = none 

in the past year, 1 = 1 or 2 days in the past 

year, 2 = once a month or less, 3 = 2 or 3 days 

a month, 4 = 1 or 2 days a week, 5 = 3 to 5 

days a week, and 6 = nearly every day), one 

for alcohol use and one for marijuana use. 

Two schooling factors tapped academic disengagement. 

The adolescents reported their 

grades (1 = D/F, 2 = C, 3 = B, and 4 = A) in 

math, science, English, and social studies in 

the past year. For each class, a binary item 

marked whether they had failed (1) or not 

(0). These four items were summed to measured 

class failures. Finally, truancy was the 

count of adolescent-reported unexcused 

absences from school (0 = 0 days, 1 = 1–2 

days, 2 = 3–5 days, 3 = 6–9 days, and 4 = 10 

or more days). Again, Wave I and Wave II versions 

of these variables were created. 

Prevalence of Obesity in School Because 

the Wave I in-home interview was conducted 

for a sizable representative subsample in each 

school, the obesity measure just described 

could be used to estimate the proportion of 

the student body of each school who was at 

or above the BMI threshold for obesity. To be 

more specific, I used this measure to identify 

all obese boys and girls in the sample and 

then averaged these two measures across all 

the students in each study school to calculate 

the proportion of students in each school 

who fit these two categories. Their sum within 

each school represents the total proportion 

of obese students in the school. 

Selection Factors Controlling for Wave I 

measures of characteristics that covary with 

obesity and predict psychosocial adjustment 

and college enrollment guards against the 

observation of spurious associations. The first 

category of such selection factors, demographic 

characteristics, includes grade level, 

race/ethnicity (dummy variables for non- 

Latino/Latina white, non-Latino/Latina 

African American, Latino/Latina, Asian

250 Crosnoe 

American, and other race/ethnicity), family 

structure (1 = adolescent lived with both biological 

parents, 0 = another family form), and 

parental education (1 = less than high school 

graduation, 2 = high school graduate, 3 = 

some college, 4 = college graduate, 5 = postgraduate—maximum 

level taken in two-parent 

families). 

The second category, school characteristics, 

includes four factors. School level (1 = 

middle school, 0 = high school or comprehensive 

school) and sector (1 = private, 0 = 

public) were based on the reports of school 

administrators at Wave I. Because the In- 

School Survey was a near-census of each 

school, reports of parental education and 

race/ethnicity in this survey could be aggregated 

within schools to measure the proportion 

of students in each school who were 

nonwhite and had at least one parent with a 

college degree. The third category, adolescents’ 

characteristics, includes measured ability 

(percentile scores on a modified version of 

the Peabody Picture Vocabulary Test) and athletic 

status (1 = adolescent on a sports team 

at school, 0 = no participation). The fourth 

category, social relationships, includes two 

network-derived items: number of friends (a 

count of friends nominated by the adolescent) 

and involvement with friends (the sum 

of whether or not the adolescent had hung 

out with, talked with, spent time with, or visited 

the nominated friend in the past week, 

averaged across all nominated friends). 

Romantic involvement was a binary measure 

(1 = adolescent reported having a serious 

boyfriend or girlfriend, 0 = no such relationship). 

Plan of Analyses 

The analyses proceeded in two general steps, 

with some preliminary and ancillary analyses 

along the way. In the first step, Wave III college 

enrollment was regressed on the Wave I 

selection factors, adolescent obesity, the two 

measures of the prevalence of obesity in the 

school, and then interactions between adolescent 

obesity and the two school obesity 

measures. The results of these models 

assessed differences in college enrollment 

after high school by obesity category during 

middle school or high school and the degree 

to which these differences varied according 

to the proportion of the student body of the 

school made up of obese girls or boys. 

If these initial models revealed significant 

obesity-related differences in college enrollment, 

then the second step was to investigate 

possible psychosocial mediators of these differences. 

This investigation required that obesity 

be associated with the psychosocial factors 

(Path A1 in Figure 1); that the psychosocial 

factors be associated with college enrollment 

(Path A2 in Figure 1); and, finally, that 

taking the psychosocial factors into account 

attenuated the initially observed associations 

between obesity and college enrollment. To 

establish mediation in this way, the six Wave 

II psychosocial adjustment factors were first 

regressed on Wave I selection factors (including 

the Wave I version of the psychosocial factors), 

adolescent obesity, the two measures of 

the prevalence of obesity in the school, and 

then interactions between adolescent obesity 

and the two school obesity measures. These 

models demonstrated whether obesity was 

associated with changes in six psychosocial 

problems over a one-year period and whether 

these associations varied by the proportion of 

obese boys and girls in school (testing Paths 

A1 and B in Figure 1). Next, the original college-enrollment 

models were expanded to 

include these six Wave II psychosocial factors. 

2 The results of these models gauged the 

degree of association between psychosocial 

adjustment during secondary school and college 

enrollment after secondary school (testing 

Path A2 in Figure 1). Moreover, a comparison 

of the obesity coefficients and the 

obesity x school obesity interaction terms 

before and after the inclusion of these psychosocial 

factors revealed the degree to 

which the association between Wave I obesity 

and Wave III college enrollment—and the 

school-by-school variation in this association—was 

explained by psychosocial adjustment 

at Wave II. 

All the models were estimated separately 

for boys and girls with the mixed procedure, 

the SAS version of multilevel modeling 

(Singer 1998). Multilevel techniques provided 

the most accurate estimates of school effects 

and corrected the violations of independence


that were created by the school-based sampling 

frame (Chantala and Tabor 1999). 

Because all the outcomes that were examined 

in this study were either binary, count, or 

countlike ordinal variables, the glimmix 

macro was used to convert the mixed procedure 

to logistic and Poisson regression. All the 

models contained a random intercept for the 

outcome and a random slope for obesity, and 

all the variance components were statistically 

significant (p < .001). 

RESULTS 

A Profile of Obese Adolescents and 

Their Peers 

Adolescents whose BMI reached the threshold 

for obesity at Wave I differed from their 

nonobese peers in multiple ways, both during 

and after their secondary school careers. Most 

important, only 23 percent enrolled in college 

after high school, compared to 35 percent 

of their nonobese peers. They were also 

more likely to be boys (57 percent versus 46 

percent of nonobese youths) and racial/ethnic 

minorities (45 percent were African 

American or Latino/Latina versus 36 percent 

of nonobese youths). Moreover, the average 

level of parental education was in the range 

of a high school diploma (2.79) for the obese 

youths but in the range of some college 

(3.00) for the nonobese youths. Finally, the 

obese adolescents had significantly higher 

mean levels of self-rejection, class failures, and 

truancy than did their nonobese peers, but 

they did not differ significantly from these 

peers in terms of suicidal ideation or substance 

use. 

Obesity and College Enrollment 

The descriptive statistics just presented provide 

preliminary evidence that young people 

who were obese during middle school and 

high school had lower rates of college matriculation 

in the period after high school than 

did their peers. Moreover, they suggest that 

the fewer socioeconomic resources and 

greater psychosocial risks of obese youths 

may be reasons for these lower rates. Table 2 

presents the results of logistic regressions that 

more accurately estimate the longitudinal 

association between adolescents’ obesity and 

college enrollment, net of numerous demographic, 

school, adolescent, and social characteristics 

that are associated with both obesity 

and educational attainment. 

For the boys, obesity at Wave I was not significantly 

associated with being enrolled in, or 

having graduated from, college at Wave III 

once the four sets of selection factors (demographic, 

school, adolescent, social) were 

taken into account. Additional analyses not 

shown in Table 2 revealed no significant interaction 

between boys’ obesity and the proportion 

of obese boys (or girls) in the school. For 

the girls, however, obesity at Wave I was associated 

with significantly lower odds of attending 

college at Wave III. Obese girls had 50 

percent lower odds (1 – OR x 100) of being in 

(or having graduated from) college during 

this period than did nonobese girls, net of all 

four sets of Wave I selection factors. 

Again, two interaction terms were added 

to this base model: obesity x the proportion 

of obese boys in the school and obesity x the 

proportion of obese girls in the school. The 

former was not statistically significant, but the 

latter was (p < .001). Because odds ratios are 

not appropriate for interaction terms, I used 

the significant logistic coefficients of obesity, 

proportion of obese girls in the school, and 

the interaction between the two to calculate 

the predicted odds of college enrollment for 

different groups of girls. Specifically, I calculated 

the odds of Wave III college enrollment 

for obese and nonobese girls who went to 

schools with no other obese girls and who 

went to schools in which 10 percent of the 

student body was made up of obese girls 

(one standard deviation above the mean on 

this school variable). In doing so, I held all 

other variables in the model to their sample 

means. 

Nonobese girls generally had better odds 

(about 18 percent higher) of attending college 

when they attended schools in which no 

obese girls could be found in the student 

body than when they attended schools in 

which obese girls made up a relatively large 

proportion of the student body. Conversely, 

obese girls generally had better odds (about

252 Crosnoe 

Table 2. Odds Ratios from the Logistic Regressions Predicting Wave III College Enrollment, by 

Gender (n = 4,865 girls and 4,055 boys) 

Variable Odds Ratios for Boys Odds Ratios for Girls 

Obesity .83 .50*** 


Grade level 1.07* 1.05 

Non-Latino/Latina white a — — 

African American 1.30+ 1.26* 

Latino/Latina 1.09 1.02 

Asian American 1.86*** 1.28 

Other race/ethnicity 1.20 .54* 

Family structure (two parent) 1.97*** 1.82*** 

Parental education 1.46*** 1.60*** 

School level (middle) 1.15 1.03 

School sector (private) .91 1.35 

School minority representation 1.01+ 1.01*** 

School proportion of college-educated. parents 10.09*** 9.58*** 

Measured ability 1.02*** 1.02*** 

Athletic status 1.80*** 1.57*** 

Number of friends 1.02 .98 

Involvement with friends 1.01 .97 

Romantic involvement .84* .84* 


Proportion of obese girls .32 .17 

Proportion of obese boys .07 .42 

a White was the comparison category for the race/ethnicity dummy variables. 

*** p < .001, ** p < .01, * p < .05, + p < .10. 

50 percent higher) of attending college when 

they attended schools in which obese girls 

were more numerous than when they attended 

schools in which they were the sole obese 

girl. Taking a different angle, obese and 

nonobese girls had roughly the same odds of 

attending college after high school if they 

attended schools in which obese girls were 

well represented, but nonobese girls had 

much better odds (about 50 percent higher) 

of doing so than did obese girls if they 

attended schools in which obese girls were 

rare. 3 

As was already mentioned, different demographic 

groups have different standards for 

appearance, in general, and body size, in particular, 

and they also differ in expectations for 

and support of educational attainment. Thus, 

as an ancillary analysis, I estimated the same 

models for white, African American, 

Latino/Latina, and Asian American youths, 

respectively, and then for youths with college-educated 

and non-college-educated 

parents (results not shown). The results for 

boys were essentially the same, regardless of 

race/ethnicity or level of parental education. 

The results for girls demonstrated more variation. 

Although significance levels varied 

according to the sample size (e.g., ranging 

from 2,641 white girls to 319 Asian American 

girls), the basic results were surprisingly similar 

across the groups. Yet, the longitudinal 

association between obesity and college 

enrollment and the moderating effect of the 

proportion of obese girls in the school were 

slightly more pronounced among racial/ethnic 

minority girls (versus whites) and among 

girls with college-educated parents (versus 

those with parents who did not graduate 

from college).


Obesity and Psychosocial 

Adjustment 

The previous analyses revealed that girls who 

were obese during adolescence were less likely 

to enroll in college in young adulthood, 

especially when they attended middle schools 

and high schools in which obesity was relatively 

uncommon. Because obese and 

nonobese boys did not differ in terms of college 

enrollment after high school no matter 

where they attended middle school or high 

school, the subsequent investigation of the 

psychosocial mechanisms linking obesity to 

college enrollment will focus only on girls. As 

was described in the Plan of Analyses, testing 

mediation first required that I establish that 

obesity predicts the psychosocial outcomes. 

Table 3 presents the odds ratios from logistic 

regressions predicting two internalizing 

symptoms—self-rejection and suicidal 

ideation—among girls only. As may be seen 

in the first column, obesity at Wave I was 

associated with greater odds of self-rejection 

at Wave II, net of the four sets of selection factors, 

as well as the Wave I version of self-rejection. 

4 Specifically, the odds of self-rejection 

were 63 percent higher for obese girls than 

for nonobese girls. Because of the control for 

Wave I self-rejection, these odds capture 

increases in the odds of self-rejection between 

Wave I and Wave II that are associated with 

obesity. As may be seen in the second column, 

obesity was also associated with 

increased odds of suicidal ideation among 

girls. Additional modeling iterations included 

interaction terms between adolescent obesity 

and the two school-level obesity factors. No 

interaction term significantly predicted either 

of the two internalizing outcomes for girls. 

The first panel in Table 4 presents the 

exponentiated coefficients from Poisson models 

predicting two types of self-medication— 

alcohol and marijuana use—among girls. 

Obesity was associated with higher expected 

frequencies of both. After all selection factors, 

as well as the Wave I versions of both substance 

use variables, were controlled for, 

these two obesity coefficients just narrowly 

missed statistical significance at the .05 level. 

Again, adding interaction terms between 

obesity and the two school-level obesity fac- 

tors revealed no significant across-school variations 

in the associations between Wave I 

obesity and either form of Wave II substance 

use for girls. 

Finally, the second panel in Table 4 presents 

the exponentiated coefficients from Poisson 

models predicting two types of academic disengagement—the 

number of class failures and 

truancy—among girls. Obesity at Wave I predicted 

class failures at Wave II, net of the selection 

factors and Wave I number of class failures. 

More specifically, obese girls had 24 percent 

higher expected frequencies of class failures 

between Wave I and Wave II than did their 

nonobese counterparts who had the same 

number of class failures at Wave I. Additional 

analyses revealed no significant interaction 

between girls’ obesity and either school-level 

obesity factor. Wave I obesity did not predict 

girls’ truancy rates at Wave II in the full sample. 

Yet, in the next modeling iteration (not shown), 

the obesity measure did interact significantly (p 

< .05), with the school-level factor indexing the 

proportion of obese girls in the student body. 

To interpret this interaction term, I again calculated 

the predicted frequencies of truancy for 

obese and nonobese girls in different types of 

schools. Nonobese girls did not differ in their 

rates of truancy across schools. On the other 

hand, obese girls had low truancy rates if they 

attended schools in which at least 10 percent of 

the student body was other obese girls, but 

they had the highest truancy rates of all girls 

when they attended schools with no other 

obese girls. 

In sum, girls who were obese at Wave I 

demonstrated significant or marginally significant 

increases in self-rejection, suicidal ideation, 

alcohol use, marijuana use, and class failure 

between Wave I and Wave II. At the same time, 

they demonstrated increases in truancy during 

this period if they attended schools with a low 

representation of obese girls in the student 

body. The results provide support—among girls 

at least—for Path A1 in Figure 1. 

Obesity, Psychosocial Adjustment, 

and College Enrollment 

Having confirmed Path A1, I now attempt to 

establish Path A2. Table 5 presents the partial 

results of a logistic regression model predict-

254 Crosnoe 

Table 3. Results from Logistic Regressions Predicting Wave II Internalizing Symptoms Among 

Girls (n = 4,910) 

ing college enrollment at Wave III. These 

results reveal the extent to which the six psychosocial 

factors predicted college enrollment. 

Moreover, the obesity effect can be 

compared to the corresponding effect in 

Table 2 to determine the extent to which the 

addition of the six psychosocial factors attenuated 

previously observed associations 

between obesity and college enrollment. 

All six markers of psychosocial adjustment 

at Wave II predicted lower odds of college 

enrollment at Wave II among girls, net of the 

four sets of selection factors. Five of these six 

odds ratios were statistically significant at 

conventional levels, and the sixth (self-rejection) 

was marginally significant. Comparing 

the odds ratio of Wave I obesity in this table 

to the corresponding ratio in Table 2 revealed 

that the association between Wave I obesity 

and Wave III college enrollment among girls 

was attenuated by just over one-third by the 

inclusion of these psychosocial factors. The 

previously observed interaction between girls’ 

obesity and the proportion of obese girls in 

the student body was essentially unchanged 

by the inclusion of the six psychosocial factors 

(interactions not shown). 

DISCUSSION 

Odds Ratios 

Self- Suicidal 

Variable Rejection Ideation 

Obesity 1.63*** 1.25** 


Grade level .99 .99 

Non-Latino/Latina white a — — 

African American .87 .87 

Latino/Latina .76 .76 

Asian American .76 .76 

Other race/ethnicity 1.17 1.17 

Family structure (two parent) 1.07 1.07 

Parental education 1.01 1.01 

School level (middle) 1.03 1.03 

School sector (private) 1.42 1.42 

School minority representation 1.01* 1.01* 

School proportion of college-educated parents .48 .48 

Measured ability .99 .99 

Athletic status .71** .71** 

Number of friends 1.02 1.02 

Involvement with friends .94 .95 

Romantic involvement 1.05 1.05 

Wave I version of outcome 11.25*** 11.36*** 


Proportion of obese girls .29 .63 

Proportion of obese boys 1.37 4.39 


*** p < .001, ** p < .01, * p < .05, + p < .10. 

Obesity does not immediately come to mind 

when one thinks about academically at-risk 

youths in the educational system, but the 

results of the study suggest that, at least for 

adolescent girls, obesity is indeed an academ-


Table 4. Results from Poisson Regressions Predicting Wave II Self-Medication and Academic 

Disengagement Among Girls (n = 4,865) 

ic risk factor that is on par with other demographic, 

behavioral, and cognitive factors 

that have received so much attention. This 

risk status of obesity touches on one of the 

fundamental tenets of contemporary sociological 

research on education, namely, that 

the youth culture that emerges within the 

educational system filters into the formal 

processes of schools in ways that shape academic 

outcomes (Coleman 1961; Eder et al. 

1995). 

To elucidate the ways in which the social 

side of schooling creates academic consequences 

out of nonacademic personal circumstances, 

I returned to two core traditions 

of 20th-century sociology. Integrating 

insights from Cooley’s ([1902]1983) looking- 

Exponentiated Coefficients Exponentiated Coefficients 

for Self-Medication for Disengagement 

Alcohol Marijuana 

Variable Use Use Failures Truancy 

Obesity 1.13+ 1.17+ 1.24** 1.04 


Grade level 1.05** .93* .79*** 1.40*** 

Non-Latino/Latina white a — — — — 

African American .83* .80* 1.08 .84* 

Latino/Latina 1.12 1.21* 1.10 .98 

Asian American .86 .99 .95 1.12 

Other race/ethnicity .95 1.22 .99 1.16 

Family structure (two parent) .98 .84** .89* .78*** 

Parental education .99 .99 .90*** .90*** 

School level (middle) 1.04 .90 1.24* 1.22*** 

School sector (private) .90 .90 .55** .85 

School minority representation .99 .99 1.01 .99 

School proportion of 

college-educated parents 1.17 1.08 .57 .86 

Measured ability .99 1.01 .99*** .99*** 

Athletic status 1.10* 1.03 .89* .81*** 

Number of friends .99 1.02 .99 1.01 

Involvement with friends 1.12*** 1.08** .97 1.10*** 

Romantic involvement 1.28*** 1.54*** .93 1.23*** 

Wave I version of outcome 1.45*** 1.39*** 1.60*** 1.42*** 


Proportion of obese girls .90 3.06 1.21 .46 

Proportion of obese boys 1.52 .51 .26 13.07 


*** p < .001, ** p < .01, * p < .05, + p < .10. 

glass self and Goffman’s (1963) social stigma 

perspectives, as well as their descendants, my 

conceptual model asserted that the stigma 

attached to obesity in American culture creates 

a climate of negative social feedback, 

either real or perceived, for obese youths, 

especially when that larger social stigma is 

echoed and reinforced in the peer cultures of 

the youths’ schools. Such feedback, in turn, 

can trigger problematic psychosocial responses 

as obese youths try to escape this stigma, 

alleviate the stress associated with it, or simply 

accept it as legitimate. Because psychosocial 

adjustment cannot be divorced from academic 

progress, these responses to the stigma 

of obesity disrupt the educational attainment 

of obese youths. Considering that edu-

256 Crosnoe 

Table 5. Selected Results from Final Logistic Regressions Predicting Wave III College 

Enrollment Among Girls (n = 4,865 girls) 

Variable Odds Ratios 

Obesity .67** 

Wave II Adjustment Factors 

Self-rejection .82+ 

Suicidal ideation .79* 

Alcohol use .94* 

Marijuana use .90*** 

Class failures .48*** 

Truancy .91*** 

Note: This model also controlled for grade level, race/ethnicity, family structure, parental education, 

school level, school sector, school minority representation, school proportion of collegeeducated 

parents, measured ability, athletic status, number of friends, involvement with friends, 

romantic involvement, proportion of obese girls in the school, and proportion of obese boys in 

the school. 

*** p < .001, ** p < .01, * p < .05, + p < .10. 

cational attainment is a foundation of the 

adult life course, including health and mortality, 

this role of obesity in education has farreaching 

consequences (Kerckhoff 1993; 

Mirowsky and Ross 2003). 

An analysis of longitudinal, nationally representative 

data supported this conceptual 

model for girls only. Obese girls were less likely 

to enter college after high school than were 

their nonobese peers. This association 

between obesity during middle school or 

high school and college enrollment after high 

school persisted despite controls for numerous 

demographic, school, adolescent, and 

social factors that could conceivably select 

girls into obesity and truncated educational 

trajectories and held across several different 

binary and categorical measurements of the 

dependent variable. Finally, the association 

between obesity and college enrollment was 

particularly strong among nonwhite girls with 

non-college-educated parents and in schools 

with few obese girls in the student body. 

To unpack this finding, I considered several 

different psychosocial mediators. First, obesity 

significantly predicted increasing rates of 

self-rejection, suicidal ideation, and class failure 

and marginally predicted increasing rates 

of alcohol and drug use. Moreover, obesity 

predicted increasing rates of truancy in 

schools in which obesity was rare. Second, 

suicidal ideation, alcohol use, drug use, class 

failure, and truancy all significantly predicted 

lower odds of matriculating in college after 

high school, and self-rejection marginally predicted 

these lower odds. Third, adjusting for 

psychosocial factors reduced the association 

between obesity and college enrollment. This 

adjustment did not eliminate this association, 

nor did it alter the strength of the interaction 

between obesity on the individual and school 

levels. Thus, the psychosocial correlates of 

obesity explained a part of the link between 

obesity and college enrollment, in general, 

but did not explain the greater strength of 

this link in schools in which obesity was rare. 

In other words, psychosocial adjustment 

appeared to be more important to understanding 

the educational risks of the general 

stigma of obesity in American youth culture 

than to understanding why these risks may 

vary as a function of the local intensity of this 

stigma in specific school-based cultures. 

That girls, rather than boys, seem to be 

more vulnerable to the nonhealth risks of obesity 

echoes two prominent themes in research 

and theory on gender. The first is that the body 

and appearance are more central to the selfconcept 

of girls than of boys, usually in 

unhealthy and damaging ways (see Brumberg’s 

1997 book The Body Project). The findings of 

my study extend that rich literature by demonstrating 

that the psychosocial side of the body 

project has consequences that extend beyond


mental health and interpersonal functioning. 

Indeed, it appears to disrupt educational attainment 

in ways that counter many of the concrete 

advantages that girls have in the educational 

system. As obesity rates increase, the 

ramifications of this gender inequality among 

obese youths may become more pronounced 

in the general population. The second theme is 

that psychosocial phenomena (e.g., self-perceptions 

and cultural norms) have greater 

effects on the education of girls than of boys, 

often in ways that hurt girls (see Correll 2001). 

The findings add that self- and other assessments 

of nonacademic traits, such as obesity, 

can undermine girls’ educational careers in 

much the same way as girls’ misperceptions of 

their ability. 

In the future, this conceptual model should 

be tested more comprehensively and then 

elaborated. As one example, the social stigma 

of obesity that is the linchpin of the conceptual 

model was not tested directly in this study. 

Instead, it was implied by associations between 

obesity and aspects of psychosocial maladjustment. 

Add Health does not contain good data 

on what young people think about obesity or 

on teasing, bullying, social aggression, and 

other clear manifestations of social stigma. One 

possible opportunity for unpacking the social 

stigma of obesity is the upcoming adolescent 

extension of the long-running NICHD Study of 

Early Child Care, which has rich data on peer 

victimization, obesity, and education. Qualitative 

research will also be useful in figuring out 

how obesity is perceived and treated among 

young people in school. As another example, 

the peer context was measured somewhat 

bluntly here as the general characteristics of the 

full student body. Peer networks, which are 

available in Add Health, and course-taking 

groups, which are now available through the 

educational supplement to Add Health (see 

Muller et al. 2007), may be more appropriate 

levels of analysis for understanding local peer 

cultures. 

Another limitation of the study that needs 

to be addressed in the future is the unexplained 

portion of the association between 

obesity and college enrollment. Although the 

aspects of psychosocial adjustment that were 

considered here did factor into the lower 

rates of college enrollment among obese 

girls, some other mechanisms may have been 

at work, possibly poor health, missed course 

work related to poor health, or discrimination. 

These possibilities must eventually be 

examined in tandem with, and in contrast to, 

psychosocial pathways to determine the best 

ways to help obese youths in their educational 

endeavors. 

In closing, I want to connect this study to 

two larger themes in research on education and 

health. First, academic progress is about more 

than navigating the formal curriculum; it is also 

about connecting to others and, through these 

connections, developing a sense of self 

(Wigfield and Eccles 2002). Thus, how students 

fit in with or stand out from their peers at 

school is of the utmost importance to their educational 

pathways. Rather than chalking up the 

social challenges of school life as harmless parts 

of growing up, therefore, we need to assess the 

risks that are associated with these challenges 

as ways of easing passage through the educational 

system (Eder et al. 1995; McFarland 

2001). Second, the enormous literature on the 

physical consequences of obesity has generated 

alarm among many scientists about the 

future health of the American public, which, in 

turn, has generated a backlash in certain sectors 

that this alarm is overstated (see Campos et al. 

2006 and counterpoints for an overview of this 

debate). Regardless of who “wins” this debate, 

this study and studies like it (see Brownell et al.’s 

2005 Weight Bias) suggest a reconsideration of 

the implications of rising rates of obesity. If the 

public health threat is so severe, then the psychosocial 

consequences of obesity that have 

been documented here suggest a double disadvantage 

of obesity in the future. If the public 

health threat is overblown, then these consequences 

are the real risks of obesity. In both 

cases, reducing the stigma of obesity in 

American society will enhance the lives of obese 

girls as they grow up. 

NOTES 

1. Although not pictured in the conceptual 

model, both the bidirectional nature of the 

focal pathways (e.g., psychosocial adjustment 

affecting obesity) and the alternative pathways 

(e.g., positive psychosocial responses to obesi-

258 Crosnoe 

ty) are clearly a major part of the operationalization 

of this conceptual model. 

2. Ancillary analyses were also conducted 

that included more positive responses to obesity 

(e.g., increased participation in extracurricular 

activities) as mediators. As expected, these 

inclusions did not alter the general pattern of 

results because such responses were less common 

and did not translate into educational risk. 

3. This protective effect of the proportion of 

obese girls in the school tapers off at around 20 

percent. 

4. The control for Wave I versions of the psychosocial 

factors helps to account for the possibility 

of reciprocal associations between obesity 

and psychosocial adjustment. 

REFERENCES 

Adams, Kimberly, Roger G. Sargent, Sharon H. 

Thompson, Donna Richter, Sara J. Corwin, and 

Thomas Rogan. 2000. “A Study of Body Weight 

Concerns and Weight Control Practices of 4th 

and 7th Grade Adolescents.” Ethnicity & Health 

5:79–94. 

Allon, Natalie. 1981. “The Stigma of Overweight in 

Everyday Life.” Pp. 130–74 in Psychological 

Aspects of Obesity: A Handbook, edited by 

Benjamin J. Wolman. New York: Van Nostrand 

Rheinhold. 

Anderson, Pamela S., and Kristen F. Butcher 2006. 

“Childhood Obesity: Trends and Potential 

Causes.” The Future of Children 16:19–45. 

Aseltine, Robert H., and Susan L. Gore. 2000. “The 

Variable Effect of Stress on Alcohol Use from 

Adolescence to Early Adulthood.” Substance Use 

and Misuse 35:643–68. 

Ball, Kylie, David Crawford, and Justin Kenardy. 

2004. “Longitudinal Relationships Among 

Overweight, Life Satisfaction, and Aspirations 

in Young Women.” Obesity Research 

12:1019–30. 

Bearman, Peter, Jo Jones, and J. Richard Udry. 1997. 

“The National Longitudinal Study of Adolescent 

Health: Research Design.” Available online: 

http://www.cpc.unc.edu/projects/addhealth/ 

design 

Brownell, Kelly D., Rebecca Puhl, Marlene Schwartz, 

and Leslie Rudd, eds. 2005. Weight Bias: Nature, 

Consequences, and Remedies. New York: Guilford 

Press. 

Brumberg, Joan Jacobs. 1997. The Body Project: An 

Intimate History of American Girls. New York: 

Random House. 

Cahnman, Werner. 1968. “The Stigma of Obesity.” 

Sociological Quarterly 9:283–99. 

Campos, Paul, Abigail Saguy, Paul Ernsberger, Eric 

Oliver, and Glenn Gaesser. 2006. “The 

Epidemiology of Overweight and Obesity: 

Public Health Crisis or Moral Panic?” 

International Journal of Epidemiology 35:55–60. 

Canning, Helen, and Jean Mayer. 1966. “Obesity: Its 

Possible Effect on College Acceptance.” New 

England Journal of Medicine 275:1172–74. 

—. 1967. “Obesity: An Influence on High School 

Performance.” American Journal of Clinical 

Nutrition 20: 352–54. 

Carr, Deborah, and Michael A. Friedman. 2005. “Is 

Obesity Stigmatizing? Body Weight, Perceived 

Discrimination, and Psychological Well-Being in 

the United States.” Journal of Health and Social 

Behavior 46:244–59. 

—. 2006. “Body Weight and the Quality of 

Interpersonal Relationships.” Social Psychology 

Quarterly 69:127–49. 

Cast, Alicia, Jan E. Stets, and Peter J. Burke. 1999. 

“Does the Self Conform to Views of Others?” 

Social Psychology Quarterly 62:68–82. 

Cawley, John. 2001. “Body Weight and the Dating 

Behaviors of Young Adolescents.” Pp. 174–98 

in Social Awakening: Adolescent Behavior as 

Adulthood Approaches, edited by Robert T. 

Michael. New York: Russell Sage Foundation. 

Centers for Disease Control and Prevention. 2002. 

“BMI/Body Mass Index.” Available online: 

http://www.cdc.gov/ nccdphp/dnpa/bmi 

Chantala, Kim, and Joyce Tabor. 1999. “Strategies to 

Perform a Design-Based Analysis Using the 

AddHealth Data.” Availalable online: http:// 

www.cpc.unc.edu/projects/addhealth/files/wei 

ght1.pdf 

Coleman, James. 1961. The Adolescent Society. New 

York: Free Press of Glencoe. 

Conley, Dalton, and Rebecca Glauber. 2005. 

“Gender, Body Mass, and Economic Status.” 

Working Paper Series w11343. Cambridge, MA: 

National Bureau of Economic Research. 

Cooley, Charles Horton. [1902] 1983. Human Nature 

and the Social Order. New Brunswick, NJ: 

Transaction. 

Correll, Shelley J. 2001. “Gender and the Career 

Choice Process: The Role of Biased Self- 

Assessments.” American Journal of Sociology 

106:1691–1730. 

Crandall, Christian. 1994. “Prejudice Against Fat 

People: Ideology and Self-Interest.” Journal of 

Personality and Social Psychology 66:882–94. 

Crosnoe, Robert. 2006. “The Connection Between 

Academic Failure and Adolescent Drinking in 

Secondary School.” Sociology of Education 

79:44–60.


Crosnoe, Robert, and Chandra Muller. 2004. “Body 

Mass Index, Academic Achievement, and School 

Context: Examining the Educational Experiences 

of Adolescent at Risk of Obesity.” Journal of 

Health and Social Behavior 45:393–407. 

Dance, L. Janelle. 2001. “Shadows, Mentors, and 

Surrogate Fathers: Effective Schooling as Critical 

Pedagogy for Inner-City Boys.” Sociological 

Focus 34:399–415 

Dejong, William. 1980. “The Stigma of Obesity: The 

Consequences of Naïve Assumptions 

Concerning the Causes of Physical Deviance.” 

Journal of Health and Social Behavior 21:75–87. 

Eder, Donna, Catherine Evans, and Stephen Parker. 

1995. School Talk: Gender and Adolescent 

Culture. New Brunswick, NJ: Rutgers University 

Press. 

Ewell, Fontaine, Susan Smith, Mary Pat Karmel, and 

Daniel Hart. 1996. “The Sense of Self and its 

Development: A Framework for Understanding 

Eating Disorders.” Pp. 107–33 in The 

Developmental Psychopathology of Eating 

Disorders: Implications for Research, Prevention, 

and Treatment, edited by Linda Smolak, Michael 

P. Levine, and Ruth Striegel-Moore. Mahwah, 

NJ: Lawrence Erlbaum. 

Ferraro, Kenneth F., and Jessica A. Kelley-Moore. 

2003. “Cumulative Disadvantage and Health: 

Long-Term Consequences of Obesity?” 

American Sociological Review 68:707–29. 

Freedman, David S., William H. Dietz, Sathanur R. 

Srinviasan, and Gerald S. Berenson. 1999. 

“The Relation of Overweight to Cardiovascular 

Risk Factors Among Children and Adolescents: 

The Bogalusa Heart Study.” Pediatrics 

103:1175–82. 

Ge, Xiaojia, Glen H. Elder, Jr., Mark Regnerus, and 

Christine Cox. 2001. “Pubertal Transitions, 

Perceptions of Being Overweight, and 

Adolescents’ Psychological Maladjustment: 

Gender and Ethnic Differences.” Social 

Psychology Quarterly 64:363–75. 

Giordano, Peggy C. 2003. “Relationships in 

Adolescence.” Annual Review of Sociology 

29:257–81. 

Goffman, Erving. 1963. Stigma: Notes on the 

Management of Spoiled Identity. Englewood 

Cliffs, NJ: Prentice Hall. 

Goodman, Elizabeth, Beth R. Hinden, and Seema 

Khandelwal. 2000. “Accuracy of Teen and 

Parental Reports of Obesity and Body Mass 

Index.” Pediatrics 106:52–58. 

Goodman, Elizabeth, and Robert C. Whitaker. 2002. 

“A Prospective Study of the Role of Depression 

in the Development and Persistence of 

Adolescent Obesity.” Pediatrics 109:497–504. 

Greenberg, Bradley S., Jane D. Brown, and Nancy L. 

Buerkel-Rothfuss. 1993. Media, Sex, and the 

Adolescent. Creskill, NJ: Hampton Press. 

Halpern, Carolyn T., Rosalind B. King, Selene G. 

Oslak, and J. Richard Udry. 2005. “Body Mass 

Index, Dieting, Romance, and Sexual Activity in 

Adolescent Girls: Relationships over Time.” 

Journal of Research on Adolescence 15:535–59. 

Harrison, Kristen. 2001. “Ourselves, Our Bodies: 

Thin-Ideal, Media, Self-Discrepancies, and 

Eating Disorder Symptomatology in 

Adolescents.” Journal of Social and Clinical 

Psychology 20:289–323. 

Hirschman, C. 2001. “The Educational Enrollment of 

Immigrant Youth: A Test of the Segmented- 

Assimilation Hypothesis.” Demography 38: 

317–36. 

Janssen, Ian, Wendy M. Craig, William F. Boyce, and 

William Pickett. 2004. “Associations Between 

Overweight and Obesity with Bullying 

Behaviors in School-Age Children.” Pediatrics 

113:1187–93. 

Kerckhoff, Alan C. 1993. Diverging Pathways: Social 

Structure and Career Deflections. New York: 

Cambridge University Press. 

Latner, Janet D., Albert J. Stunkard, and G. Terence 

Wilson. 2005. “Stigmatized Students: Age, Sex, 

and Ethnicity Effects in the Stigmatization of 

Obesity.” Obesity Research 13:1226–31. 

Link, Bruce G., and Jo C. Phelan. 2001. 

“Conceptualizing Stigma.” Annual Review of 

Sociology 27:363–85. 

Loh, Eng Seng. 1993. “The Economic Effects of 

Physical Appearance.” Social Science Quarterly 

74:420–38. 

Martin, Karin A. 1996. Puberty, Sexuality, and the Self: 

Boys and Girls at Adolescence. New York: 

Routledge. 

McFarland, Daniel A. 2001. “Student Resistance: 

How the Formal and Informal Organization of 

Classrooms Facilitate Everyday Forms of Student 

Defiance.” American Journal of Sociology 

107:612–78. 

Mirowsky, John, and Catherine E. Ross. 2003. 

Education, Social Status, and Health. New York: 

Aldine de Gruyter. 

Muller, Chandra, Jennifer Pearson, Catherine 

Riegle-Crumb, Jennifer Requejo, Kenneth 

Frank, Kathryn S. Schiller, R. Kelly Raley, Amy 

G. Langenkamp, Sarah Crissey, Anna 

Strassman Mueller, Rebecca Callahan, Lindsey 

Wilkinson, and Sam Field. 2007. National 

Longitudinal Study of Adolescent Health: Wave 

III Education Data. Chapel Hill: Carolina 

Population Center, University of North 

Carolina at Chapel Hill. 

National Institutes of Health. 2003. “Statistics Related 

to Overweight and Obesity.” Available online:

260 Crosnoe 

http://www.niddk.nih.gov/health/nutrit/pubs/ 

statobes.htm 

Needham, Belinda, and Robert Crosnoe. 2005. 

“Overweight and Depression During 

Adolescence.” Journal of Adolescent Health 

36:48–55. 

Pagan, Jose A., and Alberto Davila. 1997. “Obesity, 

Occupational Attainment, and Earnings.” Social 

Science Quarterly 78:756–70. 

Puhl R. M., and K. D. Brownell. 2003. “Psychosocial 

Origins of Obesity Stigma: Toward Changing a 

Powerful and Pervasive Bias.” Obesity Reviews 

4:213–27. 

Quinn, Diane M., and Jennifer Crocker. 1999. “When 

Ideology Hurts: Effects of Belief in the Protestant 

Ethic and Feeling Overweight on the 

Psychological Well-Being of Women.” Journal of 

Personality and Social Psychology 77:402–14. 

Resnick, Michael D., Peter S. Bearman, Robert W. 

Blum, Karl E. Bauman, Kathleen M. Harris, Jo 

Jones, Joyce Tabor, Trish Beuhring, Renee E. 

Sieving, Marcia Shew, Marjorie Ireland, Linda H. 

Bearinger, and J. Richard Udry. 1997. 

“Protecting Adolescents from Harm: Findings 

from the National Longitudinal Study of 

Adolescent Health.” Journal of the American 

Medical Association 278:823–32. 

Schneider, Barbara, and David Stevenson. 1999. The 

Ambitious Generation: America’s Teenagers, 

Motivated but Directionless. New Haven, CT: Yale 

University Press. 

Singer, Judith D. 1998. “Using SAS Proc Mixed to Fit 

Multilevel Models, Hierarchical Models, and 

Individual Growth Models.” Journal of 

Educational and Behavioral Statistics 24:323–55. 

Smerdon, Becky A. 1999. “Engagement and 

Achievement: Differences between African- 

American and White High School Students.” 

Research in Sociology of Education and 

Socialization 12:103–34. 

Sobol, Arthur M., and William H. Dietz. 1997. “Social 

and Economic Consequences of Overweight in 

Adolescence and Young Adulthood.” New 

England Journal of Medicine 329:1008–12. 

Strauss, Richard, and Harold Pollack. 2003. “Social 

Marginalization of Overweight Children.” 

Archives of Pediatric and Adolescent Medicine 

157:747–52. 

Wardle, Jane, Jo Waller, and Martin Jarvis. 2002. “Sex 

Differences in the Association of Socioeconomic 

Status with Obesity.” American Journal of Public 

Health 92:1299–1304. 

Wigfield, Alan, and Jacquelynne S. Eccles. 2002. “The 

Development of Competence Beliefs, 

Expectancies for Success, and Achievement 

Values from Childhood through Adolescence.” 

Pp. 173–195 in Development of Achievement 

Motivation, edited by Alan Wigfield and 

Jacquelynne S. Eccles. San Diego, CA: Academic 

Press. 

Yeung, King-To, and John Levi Martin. 2003. “The 

Looking Glass Self: An Empirical Test and 

Elaboration.” Social Forces 81:843–79. 

Robert Crosnoe, Ph.D., is Associate Professor, Department of Sociology, and Faculty Research 

Associate, Population Research Center, University of Texas at Austin. His main fields of interest are 

the life course and human development, education, health, and family. He is currently studying the 

connection between general developmental processes, including health and social relationships, 

and the educational experiences of young people and how this connection can be leveraged to 

explain demographic inequalities, especially those that are related to poverty and immigration. 

This research used data from Add Health, a program project designed by J. Richard Udry, Peter S. 

Bearman, and Kathleen Mullan Harris and funded by Grant P01-HD31921 from the National 

Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. 

Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in 

the original design of Add Health. Persons who are interested in obtaining data files from Add 

Health should contact Add Health, Carolina Population Center, 123 West Franklin Street, Chapel 

Hill, NC 27516 (www.cpc.unc.edu/addhealth/contract.html). The author acknowledges the generous 

support of the National Institute of Child Health and Human Development (R03 HD047378- 

01, PI: Robert Crosnoe; R24 HD042849, Center Grant: Population Research Center), as well as the 

William T. Grant Scholars Program. Direct correspondence to Robert Crosnoe, Department of 

Sociology and Population Research Center, University of Texas at Austin, 1 University Station 

A1700, Austin, TX 78712-1088; e-mail: crosnoe@mail.la.utexas.edu.

SOCIOLOGY EDUCATION - American Sociological Association

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?