In the past decade, there has been a proliferation of community- and school-based college readiness programs designed to increase the participation of students who have traditionally been underrepresented in higher education. However, few of these college readiness programs have been empirically evaluated. This study examines the impact of one such intervention, the College Bound, St. Louis (CB) program. Using propensity weighting and doubly robust modeling, we found CB participants were more likely to reach proficiency on the End of Course exams, to obtain at least a B grade in a number of foundational college courses, to take more AP or honors courses, and to attend a 4-year postsecondary institution than similarly situated non-participants. Future directions for evaluating similar college readiness programs are discussed.
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Minorities, low-income, and first-generation college students are not mutually exclusive groups. Studies have shown positive intercorrelations among membership within these groups, such that first-generation college students are more likely to be African-Americans, Hispanic, and come from low-income families (Darling and Smith 2007; Engle and Tinto 2008). Similarly, Hispanics and African-Americans are overrepresented among the population of low-income college students (Cho et al. 2013).
The median p values and proportion of rejections for individual categories within a baseline covariate may not exactly coincide. For each outcome, only the observations for which the outcome is observed are retained. Thus, some individual baseline variable categories may not be observed for all outcomes, allowing for differing values across categories within a covariate in the summary table displayed.
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We would like to thank TG for funding this study. We are also grateful to Lisa Orden Zarin, Meesa Olah, Nicole Rainey, Laurie Bainter, and all of the College Bound staff and coaches for their assistance and feedback, which considerably improved this article. However, any errors remain our own.
Depending on the outcome, the analysis included between 71 and 2031 comparison students, with a median of 951 comparison students per analysis. For CB students, the analysis included between 58 and 321 students, with a median of 179 CB participants per analysis. Tables 6 and 7 provide a summary of the covariate balance between CB participants and the comparison students for the continuous and categorical variables, respectively. For both tables, the first column provides the covariate, and the next three columns provide the mean or percent for the comparison group prior to weighting, the mean or percent for the comparison group after weighting, and the mean or percent for the CB participants, respectively. For the continuous variables, the table also provides the median K–S statistic, with lower values indicating greater concurrence of the CB and weighted comparison distributions, and the proportion of times that the chi square test associated with the K–S statistic was rejected, as well as the median p value for the test that the CB and weighted comparison means are the same and the proportion of times that test was rejected. For the categorical variables, the table indicates the median difference between the proportion of CB and weighted comparison observations found in each category, the median p value for a weighted chi square test of independence between the baseline covariate and CB participation and the proportion of times this test was rejected.Footnote 2
It is important to recognize that for each outcome, there are multiple values associated with the displayed balance measure stemming from the imputation process, where we created 10 sets of plausible values. For each of these 10 separate imputations, there were 10 separate balance tables for each outcome. To synthesize the information across these covariate balance tables within an outcome, we found the median for the K–S statistics, differences in proportions, and p values across these 10 imputations. To synthesize the covariate balance information across the multiple outcomes for display in the tables below, we then found the median of these values across all the outcome measures. Due to the multiple imputation process, it is possible that a covariate was balanced in one set of imputation, but not another set of imputation. Similarly, a covariate could be balanced for one outcome but not another outcome. To take into account the variability in covariate balance, we computed the proportion of times the p value was rejected across imputations and across outcomes. If the covariates were balanced across all outcomes and all sets of imputations, the proportion of times the p value was rejected would be zero. Thus, the more often the p values are rejected, the poorer our covariate balance.
As shown in Tables 6 and 7, our covariate balance was mixed, with some variables showing optimal balance (e.g., grade 8 attendance), whereas other variables were less balanced (e.g., disability status). We observed poorer covariate balance on the categorical variables than on the continuous variables, in part because some of the distributions for the categorical variables were skewed. Furthermore, in many instances where the p values were frequently rejected (thereby indicating poor covariate balance), the differences between the groups were small. For example, the results suggested that we could not achieve covariate balance between CB students and the comparison group on the graduating cohort variables, particularly with respect to the 2013–2014 cohort, where the p value was rejected approximately 85 % of the time. Yet, there was only a one percentage point difference between the post-weighted comparison group and the CB participants (i.e., 10.5 % of the post-weighted comparison group were from the 2013–2014 cohort compared to 11.7 % of the CB students). Thus, even though some covariates were not statistically balanced, the differences were not necessarily large either.
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Le, VN., Mariano, L.T. & Faxon-Mills, S. Can College Outreach Programs Improve College Readiness? The Case of the College Bound, St. Louis Program. Res High Educ 57, 261–287 (2016). https://doi.org/10.1007/s11162-015-9385-8
- College readiness programs
- Racial minority students
- Low-income students
- First-generation college students