Remedial Enrollment During the 1st Year of College, Institutional Transfer, and Degree Attainment

ABSTRACT

This study examined whether remediation enrollment during the 1st year of college influenced individuals’ college transfer and attainment and if effects varied by racial and socioeconomic subgroups. Results based on analysis of the National Longitudinal Survey of Youth of 1997 data indicated that for 2-year college students, remediation enrollment in both mathematics and English improved the likelihood of transferring to a 4-year college and earning a bachelor’s degree. For 4-year college students, however, enrolling in any postsecondary remediation—only math, only English, or both subjects—during their 1st year in college increased their chances of transferring to a 2-year college in the following years. Enrolling in at least 1 math remedial class (i.e., only math and both subjects) appeared to hinder 4-year college students from graduating on time. Subgroup analyses showed no strong evidence that remediation enrollment played a significant role in increasing or reducing the racial and socioeconomic gaps in college attainment.

KEYWORDS:

• College attainment
• institutional transfer
• marginal means weighting through stratification
• national evidence
• remediation enrollment

Acknowledgments

The author thanks Barbara Schneider, Ken Frank, Spyros Konstantopoulos, Joshua Cowen, Eric Grodsky, Thurston Domina, Hsun-Yu Chan, Brendan Swagerty, Ripsimé Bledsoe, the anonymous reviewers, and the participants at the 2015 Midwest Sociology of Education Research Symposium, the 2015 Michigan State University Collaboration Among Education Policy Students meeting, and the 2016 NLSY Postsecondary Research Network meeting, for their constructive feedback on earlier versions of this article.

Notes

1. The present study focused on examining remediation enrollment, not remediation referral or completion, for which readers can refer to the studies by Bailey et al. (2010) and Scott-Clayton and Rodriguez (2015).

2. As documented in Sparks and Malkus (2013), 24.7% of 1st-year undergraduates whose parents had a high school diploma or less reported taking remedial courses, compared with 20.4% of those whose parents had a bachelor’s degree or higher. While only 19.9% of White students reported taking remedial courses, 30.2% of Black students and 29.0% of Hispanic students did.

3. Two other experimental studies focused on examining the effect of summer bridge remedial programs (Barnett et al., 2012) and learning community components in a remedial program (Visher, Weiss, Weissman, Rudd, & Wathington, 2012).

4. The CCM code for remedial math is “32.0104,” and the code for remedial English is “32.0108.”

5. The outcome measure of earning a BA degree within 4 years was not used as a dependent variable for 2-year college students because it was very infrequent in the data (see Table 1). Similarly, the outcome measures of obtaining an AA degree within 3 years and 4 years were not used as dependent variables for 4-year college students.

6. All covariates listed in Online Appendix Table A1 were used to predict remedial treatment status in a series of bivariate multinomial logistic regression models. For 2-year college students, 31 covariates were significantly correlated with remedial treatment conditions (at the critical level of 10%), except race/ethnicity, late for school, absent from school, percentage of peers who cut classes, high school sector, and high school science pipeline; for 4-year college analysis, 37 covariates were significantly correlated, except gender and high school program.

7. In comparison with the students included in the analytic sample, those excluded from the analytic sample tended to be individuals who were less likely to share similar characteristics with their peers across treatment conditions. For example, the “no-remediation” students excluded from the 4-year college sample on average had cognitive scores that were about 0.5 standard deviations above the mean. They were likely to be high-ability students for whom it is difficult to find “matched” cases in any remediation treatment groups.

8. Additional analyses showed that the average marginal effects computed from logistics regression models were essentially very similar to those estimates from LPMs (results available upon request).

9. Across the eight subgroup analyses, the proportion of covariates remaining significantly different among the remediation treatment groups ranged from 0% to approximately 5% after the steps of propensity score stratification and weighting. In theory, 5% of the covariates could show statistical imbalance among the treatment groups at the significant level of .05 even in a properly implemented experimental study.

Additional information

Funding

This research was supported by the NLSY 1997 Postsecondary Research Network funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under award number 5R01HD061551-02 and by the Population Research Center at the University of Texas at Austin, which receives core support from the National Institute of Child Health and Human Development under the award number 5 R24 HD042849. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health.