Using a Regression Discontinuity Design to Estimate the Impact of Placement Decisions in Developmental Math
This study evaluates the effectiveness of math placement policies for entering community college students on these students’ academic success in math. We estimate the impact of placement decisions by using a discrete-time survival model within a regression discontinuity framework. The primary conclusion that emerges is that initial placement in a lower-level course increases the time until a student at the margin completes the higher-level course they were not assigned to by about a year on average but in most cases, after this time period, the penalty was small and not statistically significant. We found minor differences in terms of degree applicable and degree transferable credit accumulation between students placed initially in the lowerlevel course.
KeywordsDevelopmental math Community colleges Evaluation Discrete time hazard model Regression discontinuity design
The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A100381 to the University of Southern California. Additional support was received from an internal grant from the Advancing Scholarship in the Humanities and Social Sciences (ASHSS) Initiative of the University of Southern California, Office of the Provost. We would first like to thank Bo Kim for exceptional research assistance. Special thanks to Will Kwon and Kristen Fong for providing support in replicating the results for other colleges, and to Holly Kosiewicz for insightful feedback. The manuscript benefited substantially from the comments of the following members of the advisory committee to this project: Paco Martorell, Sarah Reber, Lucrecia Santibanez, Juan Esteban Saavedra, and Gary Painter. Lastly, we want to thank the Los Angeles Community College District, its research department, its math faculty, and its students, for their active participation in this research project.
The views contained herein are not necessary those of the Institute of Education Sciences.
- Allison, P. D. (2005). Fixed effects regression methods for longitudinal data using SAS. Cary, NC: SAS Institute.Google Scholar
- Boatman, A. & Long, B. T. (2010). Does remediation work for all students? How the effects of postsecondary remedial and developmental courses vary by level of academic preparation. National Center for Postsecondary Research Working Paper.Google Scholar
- Boylan, H., Bliss, L., & Bonham, D. (1994). Characteristics components of developmental programs. Review of Research in Developmental Education, 11(1), 1–4.Google Scholar
- Boylan, H., Bliss, L., & Bonham, D. (1997). Program components and their relationship to students’ performance. Journal of Developmental Education, 20(3), 1–8.Google Scholar
- Calcagno, J. C., & Long, B. T. (2008). The impact of remediation using a regression discontinuity approach: Addressing endogenous sorting and noncompliance. Working Paper 14194. Cambridge, MA: National Bureau of Economic Research.Google Scholar
- California Community College Chancellor’s Office. (2011). Matriculation Handbook. Retrieved from http://www.cccco.edu/Portals/4/SS/Matric/Matriculation%20Handbook%20(REV.%2009-2011).pdf.
- Fan, J., & Gijbels, I. (1996). Local polynomial modeling and its applications. London: Chapman & Hall.Google Scholar
- Fong, K., Melguizo, T., Bos, H., & Prather, G. (2013). A different view on how we understand progression through the developmental math trajectory. Policy Brief 3. California Community College Collaborative. Rossier School of Education, University of Southern California. http://www.uscrossier.org/pullias/research/projects/sc-community-college/.
- Gamse, B. C., Jacob, R. T., Horst, M., Boulay, B., Unlu, F. (2008). Reading First impact study: Final report. NCEE 2009-4038. Washington DC: National Center for Educational Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.Google Scholar
- Greenwood, M. (1926). The natural duration of cancer (Vol. 33, pp. 1–26)., Reports on Public Health and Medical Subjects London: Her Majesty’s Stationery Office.Google Scholar
- Lazarick, L. (1997). Back to the basics: Remedial education. Community College Journal, 68, 11–15.Google Scholar
- Melguizo, T., Bos, H., & Prather, G. (2013). Are community colleges making good placement decisions in their math trajectories? Policy Brief 1. California Community College Collaborative. Rossier School of Education, University of Southern California. http://www.uscrossier.org/pullias/research/projects/sc-community-college/.
- Murnane, R. J., & Willett, J. B. (2011). Methods matter: Improving causal inference in education and social science research. Oxford: Oxford University Press.Google Scholar
- National Center for Public Policy and Higher Education & Southern Regional Education Board. (2010). Beyond the Rhetoric: Improving College Readiness through Coherent State Policy. Washington, D.C.Google Scholar
- Ngo, F., Kwon, F., Melguizo, T., Bos, H., & Prather, G. (2013). Course placement in developmental mathematics: Do multiple measures work? Policy Brief 4. California Community College Collaborative. Rossier School of Education, University of Southern California. http://www.uscrossier.org/pullias/research/projects/sc-community-college/.
- Schneider, M., & Yin, L. (2011). The hidden costs of community colleges. Washington, DC: American Institutes for Research. Retrieved from http://www.air.org/files/AIR_Hidden_Costs_of_Community_Colleges_Oct2011.pdf.
- Schochet, P. A. (2006). Regression discontinuity design case study: National evaluation of early reading first. Princeton, NJ: Mathematica Policy Research.Google Scholar
- Strong American Schools. (2008). Diploma to nowhere. Retrieved from http://www.edin08.com.