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Using Panel Data to Identify the Effects of Institutional Characteristics, Cohort Characteristics, and Institutional Actions on Graduation Rates

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Abstract

Institutional graduation rates occupy a prominent place in institutional research and public policy. Graduation rates are used in the College Scorecard, state performance funding initiatives, and potentially affect a significant proportion of public institutions revenues. Despite their widespread use, research suggests that institutional graduation rates are most strongly related to students’ entering characteristics and stable institutional characteristics, but are only weakly related to characteristics institutions can directly control. One set of institutional characteristics that appears to be related to graduation rates are expenditures for instruction, academic support, student services, and institutional support. However, inconsistencies in research findings raise the possibility that estimates of the effects of expenditures on graduation rates may be biased due to omitted variables (i.e., unobserved heterogeneity). The present research uses within-/between-effects panel data models with IPEDS panel data to account for omitted variable bias and examine the effects of institutional characteristics, cohort characteristics, and institutional expenditures on graduation rates.

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References

  • Adelman, C. (1999). Answers in the tool box. Academic intensity, attendance patterns, and bachelor’s degree attainment. US Department of Education. Retrieved February 11, 2015, from http://files.eric.ed.gov/fulltext/ED431363.pdf.

  • Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. US Department of Education. Retrieved February 11, 2015, from http://files.eric.ed.gov/fulltext/ED490195.pdf.

  • Allen, W. R. (1992). The color of success: African American college student outcomes at predominantly White and historically Black colleges and universities. Harvard Educational Review,62, 26–44.

    Article  Google Scholar 

  • Allison, P. D. (2009). Fixed effect regression models (Sage University Paper Series on Quantitative Applications in the Social Sciences, No. 07-160). Los Angeles, CA: Sage.

  • Allison, P. D. (2010). Survival analysis using SAS: A practical guide (2nd ed.). Cary: SAS Institute.

    Google Scholar 

  • Allison, P. D. (2017, July 26). Using “between-within” models to estimate contextual effects. Retrieved May 23, 2018, from https://statisticalhorizons.com/between-within-contextual-effects.

  • American College Testing. (2015). ACT/SAT concordance. Retrieved February 11, 2015, from https://www.act.org/content/act/en/products-and-services/the-act/scores/act-sat-concordance.html.

  • Astin, A. W. (1997). How “good” is your institution’s retention rate? Research in Higher Education,38, 647–658.

    Article  Google Scholar 

  • Astin, A. W., & Oseguera, L. (2012). Pre-college and institutional influences on degree attainment. In A. Seidman (Ed.), College student retention: Formula for student success (2nd ed., pp. 119–146). Lanham: Rowan & Littlefield.

    Google Scholar 

  • Bailey, T., Calcagno, J. C., Jenkins, D., Leinbach, T., & Kienzl, G. (2006). Is student-right-to-know all you should know? An analysis of community college graduation rates. Research in Higher Education,47, 491–519.

    Article  Google Scholar 

  • Bartels, B. L. (2008, July). Beyond “fixed versus random effects:” A framework for improving substantive and statistical analysis of panel, time-series cross-sectional, and multilevel data. In Paper presented at the annual Political Methodology Conference, Ann Arbor, MI.

  • Bell, A., & Jones, K. (2015). Explaining fixed effects: Random effects modeling of time-series- cross-sectional and panel data. Political Science Research and methods,2, 133–153.

    Article  Google Scholar 

  • Bhaskaran, K., & Smeeth, L. (2014). What is the difference between missing completely at random and missing at random? International Journal of Epidemology,43, 1336–1339.

    Article  Google Scholar 

  • Birdsall, C. (2018). Performance management in public higher education: Unintended consequences and the implications of organizational diversity. Public Performance and Management Review,41, 669–695.

    Article  Google Scholar 

  • Bozick, R., & DeLuca, S. (2005). Better late than never? Delayed enrollment in the high school to college transition. Social Forces,84, 528–550.

    Article  Google Scholar 

  • Brooks, S. (2015). Using campus-based financial aid strategically. In D. Hossler & B. Bontrager (Eds.), Handbook of strategic enrollment management (pp. 213–227). San Francisco: Jossey-Bass.

    Google Scholar 

  • Cabrera, A. F., Burkum, K. R., & La Nasa, S. M. (2005). Pathways to a four-year degree. In A. Seidman (Ed.), College student retention: Formula for student success (2nd ed., pp. 155–214). Lanham: Rowan & Littlefield.

    Google Scholar 

  • Carey, K. (2014, December 19). Sizing up the college rating system. The New York Times. Retrieved February 10, 2015, from http://www.nytimes.com/2014/12/20/upshot/sizing-up-the-college-rating-system.html?_r=0&abt=0002&abg=1.

  • 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 at UCLA.

    Google Scholar 

  • Deming, D. J., & Walters, C. R. (2017, August). The impact of prece caps and spending cuts on U. S. postsecondary attainment. (Working paper 23736). Cambridge, MA: National Bureau of Economic Research.

  • DesJardins, S. L., McCall, B. P., Ahlburg, D. A., & Moye, M. J. (2002). Adding a timing light to the “tool box”. Research in Higher Education,43(1), 83–114.

    Article  Google Scholar 

  • Dieleman, J. L., & Templin, T. (2014). Random-effect, fixed-effects, and the within-between specification for clustered data in observational health studies: A simulation study. PLoS ONE,9(10), 1–17.

    Article  Google Scholar 

  • Dougherty, K. J., Jones, S. M., Lahr, H., Natow, R. S., Pheatt, L., & Reddy, V. (2016). Performance funding for higher education. Baltimore: Johns Hopkins University Press.

    Google Scholar 

  • Dougherty, K. J., & Reddy, V. T. (2011). The impact of state performance funding systems on higher education institutions: Research literature review and policy recommendations. Retrieved May 23, 2018, from https://ccrc.tc.columbia.edu/publications/impacts-state-performance-funding.html.

  • Dougherty, K. J., & Reddy, V. T. (2013). Performance funding for higher education: What are the mechanisms what are the impacts? ASHE Higher Education Report, 39(2). New York: John Wiley.

  • Fleming, J. (1984). Blacks in college: A comparative study of students’ success in Black and White institutions. San Francisco: Jossey-Bass.

    Google Scholar 

  • Gándara, L., & Rutherford, A. (2018). Mitigating unintended impacts? The effects of premiums for underserved populations in performance-funding policies for higher education. Research in Higher Education,58, 681–703.

    Article  Google Scholar 

  • Gansmer-Topf, A. M., & Schuh, J. H. (2006). Institutional selectivity and institutional expenditures: Examining organizational factors that contribute to retention and graduation. Research in Higher Education,47, 613–642.

    Article  Google Scholar 

  • Gasman, M. (2011, September 7). Being fair about graduation rates at Historically Black Colleges and Universities (HBCUs). Huffington Post. Retrieved February 11, 2015, from http://www.huffingtonpost.com/marybeth-gasman/hbcus-graduation-rates_b_948678.html.

  • Hagood, L. P. (2017). The financial benefits and burdens of performance funding: How incentive policies restructure state spending on higher education. Unpublished doctoral dissertation, University of Georga, Athens, GA. Retrieved May 24, 2018, from https://athenaeum.libs.uga.edu/handle/10724/37779?show=full.

  • Hauptman, A. M. (2001, February). Increasing higher education attainment in the United States: Challenges and opportunities. Paper presented at the American Enterprise Institute conference “Degrees of Difficulty: Can American Higher Education Regain Its Edge?” Washington, D.C., Retrieved February 23, 2015, from http://www.aei.org/event/100346.

  • Hausman, J. A. (1978). Specification tests in econometrics. Econometrica,46, 1251–1271.

    Article  Google Scholar 

  • Hearn, J. C. (2015). Outcomes-based funding in historical and comparative context. Lumina Issue Papers Series. Indianapolis, IN: Lumina Foundation. Retrieved May 23, 2018, from https://www.luminafoundation.org/files/resources/hearn-obf-full.pdf.

  • Hillman, N. W., & Corral, D. (2018). The equity implications of paying for performance in higher education. American Behavioral Scientist,61, 1757–1772.

    Article  Google Scholar 

  • Hillman, N. W., Tandberg, D. A., & Fryar, A. H. (2015). Evaluating the impacts of “new” performance funding in higher education. Educational Evaluation and Policy Analysis,37, 501–519.

    Article  Google Scholar 

  • Hillman, N. W., Tandberg, D. A., & Gross, J. P. K. (2014). Performance funding in higher education: Do financial incentives impact college completions? Journal of Higher Education,85, 826–857.

    Article  Google Scholar 

  • Hosch, B. J. (2008, November). Institutional and student characteristics that predict graduation and retention rates. Paper presented at the annual meeting of the North East Association for Institutional Research, Providence, RI.

  • Hu, X., & Villarreal P. (2018). Public tuition on the rise: Estimating the effects of Louisiana’s performance-based funding policy on institutional tuition levels. Research in Higher Education.

  • Ishitani, T. T. (2006). Studying attrition and degree completion behavior among first-generation college students in the United States. Journal of Higher Education,75(5), 861–885.

    Article  Google Scholar 

  • Itzkowitz, M. (2017, November 7). Why the cohort default rate is insufficient. Washington, DC: The Third Way. Retrieved May 24, 2018, from https://www.thirdway.org/report/why-the-cohort-default-rate-is-insufficient.

  • Jaquette, O., & Parra, E. E. (2014). Using IPEDS for panel analyses: Core concepts, data challenges, and empirical applications. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (pp. 467–533). New York: Springer.

    Chapter  Google Scholar 

  • Jasinowski, J. (2015, January 5). A long overdue college rating system. The Huffington Post. Retrieved February 11, 2015, from http://www.huffingtonpost.com/jerry-jasinowski/higher-education_b_6416534.html?

  • Jones, G. (2017, October 10). Expanding student success rates to reflect today’s college students. Retrieved September 7, 2017, from https://nces.ed.gov/blogs/nces/.

  • Journal of Blacks in Higher Education. (2014, November 24). Tracking Black student graduation rates at HBCUs. Journal of Blacks in Higher Education. Retrieved from http://www.jbhe.com/2014/11/tracking-black-student-graduation-rates-at-hbcus/.

  • Kelchen, R. (2018). Higher education accountability. Baltimore: Johns Hopkins University Press.

    Google Scholar 

  • Kelchen, R., & Stedrak, L. J. (2016). Does performance-based funding affect colleges’ financial priorities? Journal of Education Finance,41, 302–321.

    Article  Google Scholar 

  • Kim, M. M. (2002). Historically Black vs. White institutions: Academic development among Black students. Review of Higher Education,25, 385–408.

    Article  Google Scholar 

  • Kim, M. M., & Conrad, C. F. (2006). The impact of historically Black colleges and universities on the academic success of African-American students. Research in Higher Education,47(4), 399–427.

    Article  Google Scholar 

  • Kreighbaum, A. (2017, May 24). Trump budget would slash student aid and research. Inside Higher Education. Retrieved May 23, 2018, from https://www.insidehighered.com/news/2017/05/24/white-house-budget-includes-tens-billions-cuts-student-aid-and-research.

  • Lomax, M. L. (2015, January 1). A proposed federal college rating system could hurt disadvantaged students. The Washington Post. Retrieved September 7, 2017, from http://www.washingtonpost.com/opinions/a-proposed-federal-college-rating-system-could-hurt-disadvantaged-students/2015/01/01/572b50a8-9112-11e4-a900-9960214d4cd7_story.html.

  • Lumina Foundation for Education (2009). Going for the goal: 2008 annual report. Indianapolis, IN: Lumina Foundation for Education. Retrieved February 24, 2015, from http://www.luminafoundation.org/publications/2008_Annual_Report.pdf.

  • Mayotte, B. (2015, July 8). What the new gainful employment rule means for college students. U.S. News & World Report. Retrieved September 7, 2017, from https://www.usnews.com/education/blogs/student-loan-ranger/2015/07/08/what-the-new-gainful-employment-rule-means-for-college-students.

  • McCormick, A. C., Pike, G. R., Kuh, G. D., & Chen, P. D. (2009). Comparing the utility of the 2000 and 2005 Carnegie Classification Systems in research on students’ college experiences and outcomes. Research in Higher Education,50, 144–167.

    Article  Google Scholar 

  • McCubbins, M. D., Noll, R. G., & Weingast, B. R. (1987). Administrative procedures as instruments of political control. Journal of Law Economics and Organization,3, 243–277.

    Google Scholar 

  • Middaugh, M. F., Graham, R., & Shahid, A. (2003, June). A study of higher education instructional expenditures: The Delaware study of instructional costs and productivity. Washington, DC: U. S. Department of Education. NCES Research Report NCES 2003-161. Retrieved February 24, 2015, from http://nces.ed.gov/pubs2003/2003161.pdf.

  • Moe, T. M. (1984). The new economics of organization. American Journal of Political Science,28, 739–777.

    Article  Google Scholar 

  • Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica,46, 69–85.

    Article  Google Scholar 

  • Murnane, R. J., & Willett, J. B. (2011). Methods matter: Improving causal inference in educational and social science research. Oxford University Press.

  • National Center for Education Statistics. (2015). About IPEDS. Washington, DC: U.S. Department of Education. Retrieved March 1, 2015, from http://nces.ed.gov/ipeds/about/.

  • National Center of Education Statistics. (2018). IPEDS finance data FASB and GASB: What’s the difference? Washington D. C.: U.S. Department of Education. Retrieved May 24, 2018, from https://nces.ed.gov/ipeds/report-your-data/data-tip-sheet-distinguishing-finance-standards-fasb-gasb.

  • National Center for Education Statistics. (n. d.). IPEDS glossary. Washington, DC: U.S. Department of Education. Retrieved from http://nces.ed.gov/ipeds/glossary/.

  • Outcalt, C. L., & Skewes-Cox, T. E. (2002). Involvement, interaction, and satisfaction: The human environment at HBCUs. Review of Higher Education,25, 331–347.

    Article  Google Scholar 

  • Pike, G. R. (2013). NSSE benchmarks and institutional outcomes: A note on the importance of considering the intended uses of a measure in validity studies. Research in Higher Education,54, 149–170.

    Article  Google Scholar 

  • Pike, G. R., & Graunke, S. S. (2015). Examining the effects of institutional and cohort characteristics on retention rates. Research in Higher Education,56, 146–165.

    Article  Google Scholar 

  • Pike, G. R., Hansen, M. J., & Childress, J. E. (2014). The influences of students’ pre-college characteristics, high school experiences, college expectations, and initial enrollment characteristics on degree attainment. Journal of College Student Retention,16, 1–23.

    Article  Google Scholar 

  • Pike, G. R., Kuh, G. D., McCormick, A. C., Ethington, C. A., & Smart, J. C. (2011). If and when money matters: The relationships among educational expenditures, student engagement, and students’ learning outcomes. Research in Higher Education,52, 81–106.

    Article  Google Scholar 

  • Rabovosky, T. (2012). Accountability in higher education: Exploring impacts on state budgets and institutional spending patterns. Public Administration Review,74, 761–774.

    Article  Google Scholar 

  • Rothwell, J. (2015, September 28). Understanding the College Scorecard. Retrieved March 23, 2018, from https://www.brookings.edu/opinions/understanding-the-college-scorecard/.

  • Ryan, J. F. (2004). The relationship between institutional expenditures and degree attainment at baccalaureate colleges. Research in Higher Education,45, 97–113.

    Article  Google Scholar 

  • Schunck, R. (2013). Within and between estimates in random-effects models: Advantages and drawbacks of correlated random effects and hybrid models. Stata Journal,13, 65–76.

    Article  Google Scholar 

  • Schunck, R., & Perales, F. (2017). Within- and between-cluster effects in generalized linear mixed models: A discussion of approaches and the xthybrid command. Stata Journal,17, 89–115.

    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, 249–279.

    Article  Google Scholar 

  • StataCorp. (2017). Longitudinal data/panel data. College Station: StataCorp.

    Google Scholar 

  • Stratford, M. (2014, December 19). Education department releases draft ‘framework’ for its college ratings plan. Inside Higher Ed. Retrieved February 24, 2015, from https://www.insidehighered.com/news/2014/12/19/education-department-releases-draft-framework-its-college-ratings-plan.

  • Tandberg, D. A., & Hillman, N. W. (2014). State higher education performance funding data: Data, outcomes, and Policy Implications. Journal of Education Finance,39, 222–243.

    Google Scholar 

  • Umbricht, M. R., Fernandez, F., & Ortagus, J. C. (2017). An examination of the (un)intended consequences of performance funding in higher education. Educational Policy,31, 643–673.

    Article  Google Scholar 

  • U.S. Department of Education. (2016, March 24). New U.S. Department of Education report highlights colleges increasing access and supporting strong outcomes for low-income students. Retrieved September 8, 2017, from https://www.ed.gov/news/press-releases/new-us-department-education-report-highlights-colleges-increasing-access-and-supporting-strong-outcomes-low-income-students.

  • Webber, D. A., & Ehrenberg, R. G. (2010, April 15). Do expenditures other than instruction affect graduation and persistence rates in American higher education? (Working Paper 121). Ithaca, NY: Cornell University Higher education Research Institute.

  • Wellman, J. V. (2008). The higher education funding disconnect: Spending more getting less. Change,40(6), 18–25.

    Article  Google Scholar 

  • White House Office of the Press Secretary. (2009, July 14). Remarks by the President on the American graduation initiative. Retrieved February 23, 2015, from http://www.whitehouse.gov/the_press_office/Remarks-by-the-President-on-the-American-Graduation-Initiative-in-Warren-MI/

  • Williams, R. (2015, April 6). Panel data: Very brief overview. South bend, IN: University of Notre Dame. Retrieved September 7, 2017, from https://www3.nd.edu/~rwilliam/stats2/panel.pdf.

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  1. Indiana University School of Education, Wright Education Building, #4268, 201 Rose Avenue, Bloomington, IN, 47405, USA

    Gary R. Pike

  2. Ball State University, Muncie, IN, 47306, USA

    Kirsten R. Robbins

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Pike, G.R., Robbins, K.R. Using Panel Data to Identify the Effects of Institutional Characteristics, Cohort Characteristics, and Institutional Actions on Graduation Rates. Res High Educ 61, 485–509 (2020). https://doi.org/10.1007/s11162-019-09567-7

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