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Predicting Graduation Rates at 4-year Broad Access Institutions Using a Bayesian Modeling Approach

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Abstract

This study models graduation rates at 4-year broad access institutions (BAIs). We examine the student body, structural-demographic, and financial characteristics that best predict 6-year graduation rates across two time periods (2008–2009 and 2014–2015). A Bayesian model averaging approach is utilized to account for uncertainty in variable selection in modeling graduation rates. Evidence suggests that graduation rates can be predicted by religious affiliation, proportion of students enrolled full-time, socioeconomic status of the student body, enrollment size and institutional revenue and expenditures. Findings also demonstrate that relatively fewer variables predict institutional graduation rates for Latina/o and African American students at 4-year BAIs. We conclude with implications for policy and key recommendations for research focused on 4-year BAIs.

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Notes

  1. Percentage of missing values was less than one percent (.39). Between 0 and 4% of data were missing across variables. Data were assumed to be missing at random (MAR). All candidate variables were used in the imputation model which converged in three iterations (−2 Ln(L) = 36328.659).

  2. R Code: bai = bms(bai, burn = 100,000, iter = 50,000, nmodel = 500, mcmc = "bd", g = "UIP", mprior = "random", mprior.size = NA, user.int = TRUE, start.value = NA, g.stats = TRUE, logfile = FALSE, logstep = 10,000, force.full.ols = FALSE, fixed.reg = numeric (0), data = NULL, randomizeTimer = TRUE).

References

  • Abdul-Alim, J. (2014). Scholars: Proposed college rating system penalizes Minority-Serving Institutions. Diverse issues in higher education. Retrieved September 2014, from http://diverseeducation.com/article/66685/.

  • Adelman, C. (1999). Answers in the toolbox: Academic intensity, attendance patterns, and Bachelor’s degree attainment. Washington, DC: U.S. Department of Education.

    Google Scholar 

  • Allison, P. D. (2012). Handling missing data by maximum likelihood. Retrieved from http://www.statisticalhorizons.com/wp-content/uploads/MissingDataByML.pdf.

  • Alon, S., & Tienda, M. (2005). Assessing the “mismatch” hypothesis: Differences in college graduation rates by institutional selectivity. Sociology of Education, 78(4), 294–315.

    Article  Google Scholar 

  • Arum, R., & Roksa, J. (2015). Measuring college performance. In M. W. Kirst & M. L. Stevens (Eds.), Remaking college: The changing ecology of higher education (pp. 169–189). Stanford, California: Stanford University Press.

    Google Scholar 

  • Bastedo, M. (2012). The organization of higher education: Managing colleges for a new era. Baltimore, Maryland: The Johns Hopkins University Press.

    Google Scholar 

  • Berger, J. B., & Milem, J. E. (2000). Organizational behavior in higher education and student outcomes. In J. C. Smart & W. G. Tierney (Eds.), Higher education: Handbook of theory and research (Vol. XV, pp. 268–338). Bronx, NY: Agathon Press.

    Google Scholar 

  • Bowen, W. G., Chingos, M. M., & McPherson, M. S. (2009). Crossing the finish line: Completing college at America’s public universities. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Braskamp, L. A., Trautvetter, L. C., & Ward, K. (2005). Putting students first: How colleges develop students purposefully. Bolton, MA: Anker Publishing Company.

    Google Scholar 

  • Carnegie Foundation (2014). Undergraduate profile. Carnegie Foundation. Retrieved from http://classifications.carnegiefoundation.org/descriptions/undergraduate_profile.php.

  • Carnevale, A., & Strohl, J. (2013). Separate and unequal: How higher education reinforces the intergenerational reproduction of White racial privilege. Washington, DC: Center of Education and the Workforce, Georgetown Public Policy Institute, Georgetown University.

    Google Scholar 

  • Committee for Economic Development. (2012). Boosting postsecondary education performance: A statement by the policy and impact committee of the Committee for Economic Development. Retrieved from http://www.ced.org/reports/single/boosting-postsecondary-education-performance.

  • Crisp, G., Doran, E., & Salis Reyes, N. A. (2014). Institutional characteristics predicting graduation rates at 4-year BAIs. Presentation at the 2014 EPI Retention Student Success Symposium, San Antonio, TX.

  • Crisp, G. (2017). Student flow and success at 2- and 4-year broadly accessible institutions. In X. Wang (Ed.), Studying transfer in higher education: New approaches to enduring and emerging topics. New Directions for Institutional Research (No. 170, pp. 103–113). San Francisco, CA: Wiley Periodicals, Inc.

  • De Luca, G., & Magnus, J. R. (2011). Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues. STATA Journal, 11(4), 518–544.

    Google Scholar 

  • DeAngelo, L., Franke, R., Hurtado, S., Pryor, J. H., & Tran, S. (2011). Completing college: Assessing graduation rates at four-year institutions. Los Angeles: Higher Education Research Institute, UCLA.

    Google Scholar 

  • Deil-Amen, R. (2015). The traditional college student: A smaller and smaller minority and its implications for diversity and access institutions. In M. W. Kirst & M. L. Stevens (Eds.), Remaking college: The changing ecology of higher education (pp. 134–165). Stanford, California: Stanford University Press.

    Google Scholar 

  • Dougherty, K. J., Natow, R. S., Bork, R. H., Jones, S. M., & Vega, B. E. (2013). Accounting for higher education accountability: Political origins of state performance funding for higher education. Teachers College Record, 115(1), 1–50.

    Google Scholar 

  • Dougherty, K. J., Jones, S. M., Lahr, H., Natow, R. S., Pheatt, L., & Reddy, V. (2016). Looking inside the black box of performance funding for higher education: Policy instruments, organizational obstacles, and intended and unintended impacts. The Russell Sage Foundation Journal of the Social Sciences, 2(1), 147–173.

    Google Scholar 

  • Doyle, W. R. (2010). Open-access colleges responsible for greatest gains in graduation rates. San Jose, CA: The National Center for Public Policy and Higher Education.

    Google Scholar 

  • Espenshade, T. J., & Radford, A. W. (2009). No longer separate, not yet equal. Princeton, NJ: Princeton University Press.

    Book  Google Scholar 

  • Garcia, G. A. (2013). Does percentage of Latina/os affect graduation rates at 4-year Hispanic Serving Institutions (HSIs), Emerging HSIs, and non-HSIs? The Journal of Hispanic Higher Education, 12(3), 256–268. doi:10.1177/1538192712467203.

    Article  Google Scholar 

  • Glass, J. C., Jr., & Harrington, A. R. (2002). Academic performance of community college transfer students and “native” students at a large state university. Community College Journal of Research and Practice, 26, 415–430. doi:10.1080/02776770290041774.

    Article  Google Scholar 

  • Goenner, C. F., & Snaith, S. M. (2004). Accounting for model uncertainty in the prediction of university graduation rates. Research in Higher Education, 45(1), 25–41.

    Article  Google Scholar 

  • Goldrick-Rab, S., & Kendall, N. (2014). Redefining college affordability: Securing America’s future with a free two year college option. Retrieved from http://wihopelab.com/publications/Redefining_College_Affordability.pdf.

  • Hamrick, F. A., Schuh, J. H., & Shelley, M. C. (2004). Predicting higher education graduation rates from institutional characteristics and resource allocation. Education Policy Analysis Archives, 12, 19. Retrieved May 4, 2004, from http://epaa.asu.edu/epaa/v12n19/.

  • Heck, R. H., Lam, W. S., & Thomas, S. (2014). State political culture, higher education spending indicators, and undergraduate graduation outcomes. Educational Policy, 28(1), 3–39. doi:10.1177/0895904812453996.

    Article  Google Scholar 

  • Heil, S., Reisel, L., & Attewell, P. (2014). College selectivity and degree completion. American Educational Research Journal, 51(5), 913–935.

    Article  Google Scholar 

  • Horn, L., & Carroll, C. D. (2006, October). Placing college graduation rates in context: How 4-year college graduation rates vary with selectivity and the size of low-income enrollment (NCES 2007-161). Retrieved from U.S. Department of Education, National Center for Education Statistics website http://nces.ed.gov/pubs2007/2007161.pdf.

  • Horn, C., Crisp, G., Cook, L., & Kuczynski, M. T. (2015). Classifying four-year broad access institutions. Presentation at the Annual Meeting of the American Educational Research Association, Chicago, IL.

  • Hoxby, C. M. (2009). The changing selectivity of American colleges. The Journal of Economic Perspectives, 23(4), 95–118.

    Article  Google Scholar 

  • Hurtado, S. (2009). Foreword. Harvard Educational Review, 79(4), 545–549.

    Google Scholar 

  • Jones, T. (2014). Performance funding at MSIs: Considerations and possible measures for public Minority-Serving Institutions. Atlanta, GA: Southern Education Foundation. Retrieved from the Lumina Foundation website https://www.luminafoundation.org/files/resources/performance-funding-at-msis.pdf.

  • Kirst, M. W., Stevens, M. L., & Proctor, K. (2010). Broad-access higher education: A research framework for a new era (December 2010 Conference Report). Retrieved from Stanford University, Center for Education Policy Analysis website http://cepa.stanford.edu/ecology/conference-report-2010.

  • Koker, M., & Hendel, D. D. (2003). Predicting graduation rates for three groups of new advanced-standing cohorts. Community College Journal of Research and Practice, 27(2), 131–146. doi:10.1080/713838115.

    Article  Google Scholar 

  • Loeb, S., Paglayan, A., & Taylor, E. (2015). Understanding human resources in broad-access higher education. In M. W. Kirst & M. L. Stevens (Eds.), Remaking college: The changing ecology of higher education (pp. 213–236). Stanford, California: Stanford University Press.

    Google Scholar 

  • Manly, C. A., & Wells, R. S. (2015). Reporting the use of multiple imputation for missing data in higher education research. Research in Higher Education, 56, 397–409.

    Article  Google Scholar 

  • Mattern, K. D., Patterson, B. F., & Wyatt, J. N. (2013). How useful are traditional admission measures in predicting graduation within four years? (Research Report 2013-1). Retrieved from http://research.collegeboard.org/sites/default/files/publications/2013/9/researchreport-2013-1-how-useful-traditional-measures-graduation.pdf.

  • McLendon, M.K., & Hearn, J.C. (2013). The resurgent interest in performance-based funding for higher education. Academe. Retrieved from https://www.aaup.org/article/resurgent-interest-performance-based-funding-higher-education#.V-Un161KXC4.

  • Melguizo, T. (2008). Quality matters: Assessing the impact of attending more selective institutions on college completion rates of minorities. Research in Higher Education, 49(3), 214–236. doi:10.1007/s11162-007-9076-1.

    Article  Google Scholar 

  • Morrison, M. C. (2012). Graduation odds and probabilities among baccalaureate colleges and universities. Journal of College Student Retention, 14(2), 157–179.

    Article  Google Scholar 

  • National Conference of State Legislatures. (2015). Performance-based funding for higher education. Retrieved from National Conference of State Legislatures website http://www.ncsl.org/research/education/performance-funding.aspx.

  • Prescott, B. (2011). Thinking anew about institutional taxonomies. Retrieved from Stanford University, Center for Education Policy Analysis website http://cepa.stanford.edu/sites/default/files/Longanecker%2011_22_11.pdf.

  • Raftery, A. E. (1995). Bayesian Model Selection in social research. In P. V. Marsden (Ed.), Sociological methodology (pp. 111–195). Cambridge, MA: Blackwell Publishers.

    Google Scholar 

  • Ryan, J. F. (2004). The relationship between institutional expenditures and degree attainment at baccalaureate colleges. Research in Higher Education, 45(2), 97–114.

    Article  Google Scholar 

  • Scott, M., Bailey, T., & Kienzl, G. (2006). Relative success? Determinants of college graduation rates in public and private colleges in the U.S. Research in Higher Education, 47(3), 249–279. doi:10.1007/s11162-005-9388-y.

    Article  Google Scholar 

  • Settersten, R. A. (2015). The new landscape of early adulthood: Implications for broad-access higher education. In M. W. Kirst & M. L. Stevens (Eds.), Remaking college: The changing ecology of higher education (pp. 113–133). Stanford, California: Stanford University Press.

    Google Scholar 

  • Stuart, G. R., Rios-Aguilar, C., & Deil-Amen, R. (2014). How much economic value does my credential have? Reformulating Tinto’s model to study students’ persistence in community colleges. Community College Review, 42(4), 327–341.

    Article  Google Scholar 

  • The White House, Office of the Press Secretary. (2009). Below are excerpts of the President’s remarks in Warren, Michigan today and a fact sheet on the American Graduation Initiative. Retrieved from http://www.whitehouse.gov/the_press_office/Excerpts-of-the-Presidents-remarks-in-Warren-Michigan-and-fact-sheet-on-the-American-Graduation-Initiative/.

  • Titus, M. A. (2006). Understanding the influence of the financial context of institutions on student persistence at four-year colleges and universities. The Journal of Higher Education, 77(2), 353–375. doi:10.1353/jhe.2006/0009.

    Article  Google Scholar 

  • University of Southern California. (2014). The Equity Scorecard Tool. Retrieved from the University of Southern California website http://cue.usc.edu/equity_model/eqs/tool.html.

  • Warner, R. M. (2012). Applied Statistics: From bivariate through multivariate techniques (2nd edition). Sage Publications.

  • Wasserman, L. (2000). Bayesian model selection and model averaging. Journal of Mathematical Psychology, 44, 92–107.

    Article  Google Scholar 

  • Wassmer, R., Moore, C., & Shulock, N. (2004). Effect of racial/ethnic composition on transfer rates in community colleges: Implications for policy and practice. Research in Higher Education, 45(6), 651–672.

    Article  Google Scholar 

  • Webber, D. A., & Ehrenberg, R. G. (2010). Do expenditures other than instructional expenditures affect graduation and persistence rates in American higher education? Economics of Education Review, 29(2010), 947–958.

    Article  Google Scholar 

  • Wellman, J. (2011). Financial characteristics of broad access public institutions. Retrieved from Stanford University, Center for Education Policy Analysis website http://cepa.stanford.edu/conference-papers/financial-characteristics-broad-access-public-institutions.

  • Zeugner, S. (2011). Bayesian Model Averaging with BMS. Retrieved from http://cran.r-project.org/web/packages/BMS/vignettes/bms.pdf.

  • Zeugner, S., & Feldkircher, M. (2015). Bayesian model averaging employing fixed and flexible priors: The BMS package for R. The Journal of Statistical Software, 68(4), 1–37. doi:10.18637/jss.v068.i04.

    Article  Google Scholar 

  • Zhang, L. (2009). Does state funding affect graduation rates at public four-year colleges and universities? Educational Policy, 23(5), 714–731. doi:10.1177/0895904808321270.

    Article  Google Scholar 

  • Zhang, L. (2010). The use of panel data models in higher education policy studies. Higher Education Handbook of Theory and Research, 25, 307–349.

    Article  Google Scholar 

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Authors and Affiliations

  1. Oregon State University, 104 Joyce Collin Furman Hall, 200 SW 15th Street, Corvallis, OR, 97331-3502, USA

    Gloria Crisp

  2. Iowa State University, Ames, IA, USA

    Erin Doran

  3. University of Hawai‘i at Mānoa, Honolulu, HI, USA

    Nicole A. Salis Reyes

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  1. Gloria Crisp
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  2. Erin Doran
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  3. Nicole A. Salis Reyes
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Correspondence to Gloria Crisp.

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See Table 5.

Table 5 Description of candidate variables
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Crisp, G., Doran, E. & Salis Reyes, N.A. Predicting Graduation Rates at 4-year Broad Access Institutions Using a Bayesian Modeling Approach. Res High Educ 59, 133–155 (2018). https://doi.org/10.1007/s11162-017-9459-x

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