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Race, Friends, and College Readiness: Evidence from the High School Longitudinal Study

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Abstract

Close friends are likely to transmit influence on students’ educational attitudes and decisions that are independent of students’ own background abilities and motivations. However, previous research suggests that close friends may have uneven effects on educational outcomes by race and ethnicity. We analyze the impact of close friends who are college bound on students’ college readiness using new and restricted panel data from the High School Longitudinal Study (2009–2011). Descriptive analyses suggest that having a college-bound friend is positively associated with college readiness and that these impacts are felt by racial and ethnic subgroups in separate and unique ways. Results from propensity score models suggest that while having a college-bound friend generally yields positive effects on all students, it has a more consistent effect on white students’ college readiness compared with Asians, blacks, and Latinos. A formal sensitivity analysis suggests that these treatment effects are robust to the confounding influence of an unobserved confounder.

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Notes

  1. AP STEM courses include any of the following: AP Math, AP Science, AP Biology, AP Chemistry, AP Physics, and AP Computer Science. The intercorrelation for the outcome variables is low (0.24) and the Cronbach’s alpha is also low (0.56). Both of these statistics suggest that combining the outcomes leads to an unreliable measure of a single construct and separating the outcomes is likely capturing multiple dimensions of college readiness.

  2. STEM enrichment programs include: Mathematics, Engineering, Science Achievement (MESA), Upward Bound, Talent Search, GEAR Up, and Advancement Via Individual Determination (AVID).

  3. These school-level STEM resources are as follows: whether the school has a special focus in math or science, whether the school partners with MESA or similar STEM enrichment program, whether the school partners with a college or university that offers a math or science summer program, whether the school sponsors a science or math summer and after school programs, whether the school pairs students with a science or math mentor, whether the school holds math or science fairs, workshops or competitions, whether the school brings in guest speakers to talk about math or science, whether the school takes students on math- or science-relevant field trips, whether the school tells students about math or science contests, websites, blogs, or other programs, whether the school or district offers incentives to attract full-time high school science or math teachers, and whether the school offers AP Calculus (AB and BC), Computer Science (A and AB), Biology, Chemistry, and Physics on-site.

  4. In order to control for heterogeneity in selection into the treatment and outcome that lies between schools, we included school-level variables in the matching model. This adjustment allows us to partially account for the clustered nature of the data and accurately specify the propensity score model (Arpino and Mealli 2008).

  5. The pstest command relies on the average treatment effect on the treated (ATT) estimation based on the psmatch2 command.

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Authors and Affiliations

  1. Cornell University, 336 Uris Hall, Ithaca, New York, 14853, USA

    Steven Elias Alvarado

  2. University of Iowa, 493 Lindquist Center North, Iowa City, IA, 52242, USA

    Brian P. An

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  1. Steven Elias Alvarado
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Correspondence to Steven Elias Alvarado.

Appendix: Rationale for Sensitivity Analysis

Appendix: Rationale for Sensitivity Analysis

The propensity score model is useful over traditional regression analysis because it can give us a sense of the extent to which we are meeting the assumption of ignorability. That is, we can formally assess how balanced observed characteristics are between students with and without CBF. When balance is high, selection bias (on observables) is low, and we can almost say that our estimated ATT mimics an experimental setting where covariates are essentially randomly distributed across students with and without CBF. Unfortunately, it is close to impossible to achieve this assumption. Therefore, we assessed our deviation from this ideal by using the pstest command in Stata, which yielded mean standardized bias estimates before and after matching. Our post-matching bias ranged from 2.56 (Asians; any AP) to 3.57 (Latinos; any AP), which are below the 5 percent bias threshold that is generally considered sufficient (Caliendo and Kopeinig 2008). The PSM model appears to have successfully reduced the bias on observed characteristics relative to traditional regression, but did not do so completely. This leaves open the possibility that unobserved covariates related to both CBF and the outcome may still bias our estimated CBF effects. Therefore, we conducted a sensitivity analysis that allowed us to assess how large an unobserved confounder, U, and its associated selection bias, must be in order to undermine our results.

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Alvarado, S.E., An, B.P. Race, Friends, and College Readiness: Evidence from the High School Longitudinal Study. Race Soc Probl 7, 150–167 (2015). https://doi.org/10.1007/s12552-015-9146-5

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