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How Resource Inequalities Among High Schools Reproduce Class Advantages in College Destinations

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Abstract

Previous studies argued that high school resources play a modest role in students’ postsecondary destinations, but they ignored schools’ programmatic resources, which provide opportunities for marks of distinction, such as Advanced Placement courses, and they focused on older cohorts of high school students who entered colleges before competition over admission to selective colleges intensified in the 1980s. Analyses of data on a cohort of students who entered college in the mid-2000s suggest that programmatic and non-programmatic resources found in high schools influence postsecondary destinations and mediates the effect of family socioeconomic status on choices among 4-year colleges.

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Notes

  1. Some scholars (Black and Smith 2004; Brand and Halaby 2006; Dale and Krueger 2002, 2011) argue that the benefits of selective colleges, or at least the certainty that there are benefits, have been overstated by previous research. Long (2008) demonstrates that the documented benefits of selective colleges survive these methodological challenges.

  2. This discussion omits studies that examined the effects of school resources on students’ postsecondary outcomes but did not fully control for either student SES or students’ academic ability, even though the authors of these studies may have had valid reasons for their designs (Halpern-Manners et al. 2009; Martin et al. 2005; Niu and Tienda 2008; Niu et al. 2006).

  3. Some studies have documented that track placement and course sequences mediate the effect of family background on enrolling in a 4-year college (Rosenbaum 1980; Schneider 2003; Schneider et al. 1998), but these studies do not address how high schools’ advanced curricula offerings mediate the effect of family background on the selectivity of the colleges students enroll in.

  4. Restricting the sample this way introduces the possibility for bias. Using sample weights minimizes bias caused by attrition (e.g., students who did not participate in the 2006 wave) and by the omission of students who did not participate in the transcript study. Dropping students because they changed high schools between the 2002 and 2004 waves introduces the possibility of biased results because the number is fairly large (1,240). In a supplemental analysis is available upon request from the author, these students were retained and data on their high school resources were, if possible, based on the averages of the high schools attended (if data on multiple high schools were not available, data from one high school was used). The results are very similar to the main analyses presented here.

  5. All sample sizes reported in this study are rounded to 10s, in compliance with NCES requirements for users of restricted-use data.

  6. I created ten imputations for student-level variables, and ten imputations for school-level variables, and merged the imputations together. School-level variables were used to impute student-level variables, and student-level variables were aggregated at the school-level and used to impute school-level variables.

  7. Using tenth grade measures risks downwardly biasing the effects of high school resources, since the tenth grade measures are potentially influenced by high school resources. In all likelihood, this is not a substantial problem; similar analyses were performed using eighth grade (pre-high school) measures from the National Education Longitudinal Study (NELS), which tracked a cohort of 1988 eighth graders. The results (not presented) are substantially similar to the ones presented here.

  8. For analyses of continuous outcomes, I use Harel’s (2009) method for averaging R 2s, which entails converting each imputation’s R 2 into a z score, taking the average of the z’s, and then converting the average z back into R 2. Unfortunately there is no such equivalent for the adjusted count R 2. The adjusted count R 2’s reported here are averages of each imputation’s adjusted count R 2 , in unweighted analyses, since it is impossible to calculate an adjusted count R 2 when sampling weights are used in Stata.

  9. Since many student- and school-level predictors are controlled for in Models 2E and 3, it is important to gauge the extent to which multicollinearity is a problem. When the same predictors are entered into a linear regression, variance inflation factors (VIFs) suggest that multicollinearity is not a problem. In model 2E, the highest VIF is 2.4, for other student SES, and for Model 3, the highest VIFs are 6 and 6.4, for the SAT dummy indicators. These VIFs are well below the threshold of ten proposed by Hocking (2003).

  10. In analyses not presented here, the effects of the guidance-counselor-to-student ratio were estimated. This resource had no significant association with college destinations. It is entirely plausible that this non-effect owes more to the difficulty of measuring the quality of the services guidance counselors provide, as opposed to these services having no influence at all.

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Acknowledgments

The author appreciates the research assistance of Robert DePhillips and Aubrey Hilbert and the helpful comments on earlier drafts of this article from Art Alderson, Jason Beckfield, Maia Cucchiara, Judson Everitt, Kim Goyette, Erin McNamara Horvat, David James, David Kirk, Annette Lareau, Jennifer C. Lee, Tania Levey, Carolina Milesi, Josipa Roksa, Robert Toutkoushian, and Pam Walters. This research was supported by a Spencer dissertation fellowship and a dissertation grant from the American Educational Research Association, the latter being funded by the National Science Foundation and the National Center for Education Statistics under NSF grant #REC-0310268.

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    Joshua Klugman

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Klugman, J. How Resource Inequalities Among High Schools Reproduce Class Advantages in College Destinations. Res High Educ 53, 803–830 (2012). https://doi.org/10.1007/s11162-012-9261-8

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