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SOCIOLOGY EDUCATION - American Sociological Association

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246 Crosnoe<br />

Halpern et al. 2005; Needham and Crosnoe<br />

2005). Even though such gender differences<br />

are less sharp in some races or social classes,<br />

the stakes of obesity are typically higher for<br />

girls, in general. Thus, although girls may be<br />

less likely than boys to take part in things like<br />

substance use or academic disengagement in<br />

general, they are probably more likely to do<br />

so in response to obesity.<br />

Second, the power of school context to<br />

condition the internalization and externalization<br />

of the stigma of obesity (Path B in Figure<br />

1) will also be stronger for girls for many of<br />

the same reasons. If girls are generally more<br />

sensitive to norms about weight, then the<br />

adjustment of obese girls should be more<br />

affected by how these girls measure up to<br />

local norms about weight, just as it is more<br />

affected by general norms. At the same time,<br />

a good deal of developmental research has<br />

demonstrated that girls are more ingrained in<br />

and reactive to social networks, especially<br />

their personal relationships, and are more<br />

likely to use feedback from significant others<br />

when they evaluate themselves (Giordano<br />

2003; Martin 1996). Thus, being out of step<br />

with schoolmates in terms of weight will have<br />

a greater impact on girls.<br />

If this conceptual model is a better fit for<br />

girls than for boys, then the psychosocial<br />

costs of obesity will be more important to<br />

understanding obesity-related educational<br />

disparities in the female population. More<br />

generally, these costs will be another disadvantage,<br />

along with discrimination and other<br />

powerful forces, that women face in the status<br />

attainment process.<br />

METHODS<br />

Data<br />

To test the conceptual model in Figure 1, I<br />

drew on nationally representative data from<br />

the National Longitudinal Study of<br />

Adolescent Health (Add Health), an ongoing<br />

study of <strong>American</strong> adolescents in Grades<br />

7–12 that began in 1994 (Bearman, Jones,<br />

and Udry 1997). With a multistage, stratified<br />

design, Add Health selected 80 schools from<br />

a comprehensive list of <strong>American</strong> high<br />

schools on the basis of their region, urbanicity,<br />

sector, racial composition, and size. Each<br />

selected school was then matched to one of<br />

its feeder schools, typically a middle school,<br />

with the probability of the feeder school<br />

being selected proportional to its contribution<br />

to the high school’s student body. The<br />

final sample included 132 schools. Nearly all<br />

the students in each school (approximately<br />

90,000) completed a limited paper-and-pencil<br />

questionnaire, the In-School Survey, in the<br />

1994–95 school year. These students became<br />

the sampling frame for three in-depth inhome<br />

interviews. A representative subsample<br />

of the In-School sample, selected evenly<br />

across high schools and pairs of feeder<br />

schools, participated in Wave I in 1995 (n =<br />

20,475), Wave II in 1996 (n = 14,736), and<br />

Wave III in 2002 (n = 15,197). The Wave II<br />

sample excluded all Wave I seniors. The Wave<br />

III sample, however, attempted to bring these<br />

Wave I seniors back in.<br />

The analytic sample for the study included<br />

all adolescents who participated in Waves I–III<br />

and had valid sampling weights (n = 10,829<br />

adolescents in 128 schools). White girls with<br />

more educated parents were slightly overrepresented<br />

in this analytic sample compared to<br />

the original Wave I sample, but the obesity<br />

rate was the same. This bias must be remembered<br />

in the interpretation of the results. Still,<br />

these selection filters were necessary—the<br />

longitudinal filter to establish the proper temporal<br />

ordering of variables in the theoretical<br />

model and the weighting filter to adjust for<br />

the complex sampling frame of Add Health<br />

(Chantala and Tabor 1999).<br />

Measures<br />

Table 1 presents descriptive statistics for all<br />

the study variables. Because of the centrality<br />

of gender to this study, these statistics are<br />

presented for the full sample and then for<br />

each gender. All significant gender differences,<br />

tested with a one-way analysis of variance<br />

(ANOVA), have been noted.<br />

College Enrollment Wave III responses to<br />

questions about schooling were collapsed<br />

into a binary measure of college enrollment.<br />

In this measure, young people who were

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