Skip to main content

Advertisement

Log in

The Impact of Centralized Advising on First-Year Academic Performance and Second-Year Enrollment Behavior

  • Published:
Research in Higher Education Aims and scope Submit manuscript

Abstract

To enhance student success, many colleges and universities have expanded academic support services and programmatic interventions. One popular measure that has been recognized as critical to student success is academic advising. Many institutions have expanded advising by creating centralized units staffed with professional advisors who serve specific student groups. In this study, I used propensity score matching to estimate the impact of using centralized academic advising at a large metropolitan public research university on undergraduate students’ first-year GPA and second-year enrollment behavior. Using a cohort of 2,745 first-time full-time freshmen who matriculated in fall 2010, I matched students who used centralized advising with those who used no advising, over the course of two semesters. I then fit an OLS regression model to examine the impact of centralized advising on first-year GPA and a Zero Inflated Negative Binomial model to examine its impact on students’ enrollment behavior in the second year. I used these parametric results to simulate average treatment effects. Results indicated that students who used centralized academic instead of no advising experienced an increase in their first-term GPA, second-term GPA, and first-year cumulative GPA. Also, students who used centralized advising during the second term experienced a decrease in their probability of first-year attrition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • 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 

  • Allen, J., Robbins, S., Casillas, A., & Oh, I.-S. (2008). Third-year college retention and transfer: Effects of academic performance, motivation, and social connectedness. Research in Higher Education, 49(7), 647–664.

    Article  Google Scholar 

  • Astin, A. W. (1970a). The methodology of research on college impact, part one. Sociology of Education, 43(3), 223–254.

    Article  Google Scholar 

  • Astin, A. W. (1970b). The methodology of research on college impact, part two. Sociology of Education, 43(4), 437–450.

    Article  Google Scholar 

  • Astin, A. W. (1991). Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education. New York: MacMillan.

    Google Scholar 

  • Aud, S., Hussar, W., Johnson, F., Kena, G., Roth, E., Manning, E., et al. (2012). The condition of education 2012. Washington, DC: U.S. Department of Education, National Center for Education Statistics.

    Google Scholar 

  • Austin, P. C. (2008). A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Statistics in Medicine, 27, 2037–2049.

    Article  Google Scholar 

  • Bahr, P. (2008). Cooling out in the community college: What is the effect of academic advising on students’ chances of success? Research in Higher Education, 49(8), 704–732.

    Article  Google Scholar 

  • Bai, H., & Pan, W. (2009). A multilevel approach to assessing the interaction effects on college student retention. Journal of College Student Retention, 11(2), 287–301.

    Article  Google Scholar 

  • Baum, S., & Payea, K. (2005). Education pays 2004: The benefits of higher education for individuals and society. Washington, DC: The College Board.

    Google Scholar 

  • Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55(4), 485–540.

    Article  Google Scholar 

  • Beatty, J. D. (1991). The National Academic Advising Association: A brief narrative history. NACADA Journal, 11(1), 5–25.

    Article  Google Scholar 

  • Becker, S. O., & Caliendo, M. (2007). Sensitivity analysis for average treatment effects. The Stata Journal, 7(1), 71–83.

    Google Scholar 

  • Board, College. (2010). Trends in college pricing. Washington, DC: The College Board.

    Google Scholar 

  • Bound, J., Lovenheim, M., & Turner, S. (2007). Understanding the decrease in college completion rates and the increased time to the baccalaureate degree. Report No 07-626. Ann Arbor, Michigan: University of Michigan Population Studies Center.

  • Brookhart, M. A., Schneeweiss, S., Rothman, K. J., Glynn, R. J., Avorn, J., & Stürmer, T. (2006). Variable selection for propensity score models. American Journal of Epidemiology, 163(12), 1149–1156.

    Article  Google Scholar 

  • Caliendo, M., & Kopeinig, S. (2005). Some practical guidance for the implementation of propensity score matching. IZA discussion paper No 1588. Bonn, Germany: Institute for the Study of Labor.

  • College Board. (2009). How colleges organize themselves to increase student persistence: Four-year institutions. New York: College Board.

    Google Scholar 

  • Cuseo, J. (2003). Academic advisement and student retention: Empirical connections and systemic interventions. Resource document. http://www.nc-access.info/Advisement_Retention_Cuseo.pdf. Accessed September 07, 2012.

  • DiPrete, T. A., & Engelhardt, H. (2000). Estimating causal effects with matching methods in the presence and absence of bias cancellation. Max Planck Institute for Demographic Research working paper 2000–2013. Rostock, Germany: Max Planck Institue for Demographic Research.

  • DiPrete, T. A., & Gangl, M. (2004). Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments. Sociological Methodology, 34, 271.

    Article  Google Scholar 

  • Dougherty, K. J., Natow, R. S., Bork, R. H., Jones, S. M., & Blanca, V. 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., & Reddy, V. (2011). The impacts of state performance funding systems on higher education institutions: Research literature review and policy recommendations. New York: Teacher College, Columbia University.

    Google Scholar 

  • Duby, P., & Schartman, L. (1997). Credit hour loads at college onset and subsequent academic performance: A multi-institutional pilot project. Paper presented at the Association for Institutional Research Forum, Orlando, FL.

  • Ensign, R. L. (2010). Fast gainers: 4 ways that colleges have raised graduation rates. Chronicle of Higher Education, 57(16), A15.

    Google Scholar 

  • GAO. (2012). Postsecondary education: Financial trends in public and private nonprofit institutions. Washington, DC: United States Government Accountability Office.

    Google Scholar 

  • Goan, S. K., & Cunningham, A. F. (2006). The investment payoff: A 50-state analysis of the public and private benefits of higher education. American Academic, 2, 23–38.

    Google Scholar 

  • Grites, T. J., Gordon, V. N., & Habley, W. R. (2008). Perspectives on the future of academic advising. In V. N. Gordon, W. R. Habley, T. J. Grites, & Associates (Eds.), Academic advising: A comprehensive handbook (2nd ed., pp. 456–471). San Francisco: Wiley.

  • Gu, X. S., & Rosenbaum, P. R. (1993). Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics, 2(4), 405–420.

    Google Scholar 

  • Guo, S., & Fraser, M. W. (2010). Propensity score analysis: Statistical methods and applications. Los Angeles: Sage.

    Google Scholar 

  • Habley, W. R. (1983). Organizational structures for academic advising: Models and implications. Journal of College Student Personnel, 24(6), 535–540.

    Google Scholar 

  • Habley, W. R. (2003). Faculty advising: Practice and promise. In G. L. Kramer (Ed.), Faculty advising: Practice and promise (pp. 23–39). Boston: Anker.

    Google Scholar 

  • Habley, W. R. (2004). The status of academic advising: Findings from the ACT Sixth National Survey (NACADA Monograph Series, Vol. 10). Manhattan, KS: National Academic Advising Association.

  • Habley, W. R., Bloom, J. L., & Robbins, S. (2012). Increasing persistence: Research-based strategies for college student success. San Francisco: Jossey-Bass.

    Google Scholar 

  • Hansen, B. B. (2004). Full matching in an observational study of coaching for the SAT. Journal of the American Statistical Association, 99(467), 609–618.

    Article  Google Scholar 

  • Hester, E. J. (2008). Student evaluations of advising: Moving beyond the mean. College Teaching, 56(1), 35–38.

    Article  Google Scholar 

  • Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007). Matching as nonparametric preprocessing for reducing model dependency in parametric causal inference. Political Analysis, 15(3), 199–236.

    Article  Google Scholar 

  • Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011). MatchIt: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 42(8), 1–28.

    Google Scholar 

  • Imai, K., King, G., & Lau, O. (2007). Zelig: Everyone’s statistical software. http://gking.harvard.edu/zelig/docs/zelig.pdf. Accessed August 31, 2012.

  • Imai, K., King, G., & Lau, O. (2008a). Toward a common framework for statistical analysis and development. Journal of Computational and Graphical Statistics, 17(4), 892–913.

    Article  Google Scholar 

  • Imai, K., King, G., & Stuart, E. A. (2008b). Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(2), 481–502.

    Article  Google Scholar 

  • Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, 86(1), 4–29.

    Article  Google Scholar 

  • Jones-White, D. R., Radcliffe, P. M., Huesman, R. L, Jr, & Kellogg, J. P. (2010). Redefining student success: Applying different multinomial regression techniques for the study of student graduation across institutions of higher education. Research in Higher Education, 51(2), 154–174.

    Article  Google Scholar 

  • Kelderman, E. (2012). With state support now tied to completion, Tennessee colleges must refocus. Chronicle of Higher Education, 59(6), A16–A18.

    Google Scholar 

  • King, M. C. (1993). Academic advising, retention, and transfer. New Directions for Community Colleges, 82, 21–31.

    Article  Google Scholar 

  • Knapp, L. G., Kelly-Reid, J. E., & Ginder, S. A. (2012). Enrollment in postsecondary institutions, fall 2010; financial statistics; fiscal year 2010; and graduation rates, selected cohorts, 2002–07. Washington, DC: US Department of Education.

    Google Scholar 

  • Light, R. J. (2001). Making the most of college: Students speak their minds. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Long, S. J. (1997). Regression models of categorical and limited dependent variables. Thousand Oaks: Sage.

    Google Scholar 

  • McCormick, A. C., & Carroll, D. C. (1999). Credit production and progress toward the bachelor’s degree: An analysis of postsecondary transcripts for beginning students at 4-year institutions. Washington, DC: US Department of Education.

    Google Scholar 

  • McPherson, M. S., & Schapiro, M. O. (Eds.). (2008). College success: What it means and how to make it happen. New York: The College Board.

    Google Scholar 

  • Melguizo, T., Kienzl, G. S., & Alfonso, M. (2011). Comparing the educational attainment of community college transfer students and four-year college rising juniors using propensity score matching methods. The Journal of Higher Education, 82(3), 265–291.

    Article  Google Scholar 

  • Metzner, B. S. (1989). Perceived quality of academic advising: The effect on freshman attrition. American Educational Research Journal, 26(3), 422–442.

    Article  Google Scholar 

  • Moreno-Serra, R. (2007). Matching estimator of average treatment effects: A review applied to the evaluation of health care programs. New York: The University of York Health, Economics and Data Group.

    Google Scholar 

  • Morgan, S. L., & Harding, D. J. (2006). Matching estimators of causal effects: Prospects and pitfalls in theory and practice. Sociological Methods & Research, 35(1), 3–60.

    Article  Google Scholar 

  • Murnane, R. J., & Willett, J. B. (2010). Methods matter: Improving causal inference in educational and social science research. New York: Oxford University Press.

    Google Scholar 

  • Murtaugh, P. A., Burns, L. D., & Schuster, J. (1999). Predicting the retention of university students. Research in Higher Education, 40(3), 355–371.

    Article  Google Scholar 

  • Myers, T. A. (2011). Goodbye, listwise deletion: Presenting hot deck Imputation as an easy and effective tool for handling missing data. Communication Methods and Measures, 5(4), 297–310.

    Article  Google Scholar 

  • OECD. (2011). Education at a glance 2011: OECD indicators. Paris: OECD.

    Google Scholar 

  • Pardee, C. F. (2000). Organizational models for academic advising. In V. N. Gordon, W. R. Habley, & Associates (Eds.), Academic advising: A comprehensive handbook (pp. 192–209). San Francisco: Jossey-Bass.

  • Pardee, C. F. (2004). Organizational structures for advising. Resource document. NACADA Clearinghouse of Academic Advising. http://www.uvm.edu/president/transform/NACADA%20Org%20Structures%20for%20Advising.pdf. Accessed October 11, 2012.

  • Pascarella, E. T., & Chapman, D. W. (1983). A multiinstitutional, path analytic validation of Tinto’s model of college withdrawal. American Educational Research Journal, 20(1), 87–102.

    Article  Google Scholar 

  • Pascarella, E. T., & Terenzini, P. T. (2005). How college affect students: A third decade of research. San Francisco: Jossey-Bass.

    Google Scholar 

  • Pietras, S. A. (2010). The impact of academic advising on GPA and retention at the community college level. Doctoral dissertation, Indiana University of Pennsylvania,

  • Radcliffe, P. M., Huesman, R. L., Jr., & Kellogg, J. P. (2006). Modeling the incidence and timing of student attrition: A survival analysis approach to retention analysis. Paper presented at the annual meeting of the Association for Institutional Research in the Upper Midwest (AIRUM), Bloomington, MN, November 2–3, 2006.

  • Reynolds, C. L., & DesJardins, S. L. (2009). The use of matching methods in higher education research: Answering whether attendance at a 2-year institution results in differences in education attainment. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 24, pp. 47–104). Dordrecht, The Netherlands: Springer.

  • Robbins, S., Allen, J., Casillas, A., Akamigbo, A., Saltonstall, M., Campbell, R., et al. (2009). Associations of resource and service utilization, risk level, and college outcomes. Research in Higher Education, 50(1), 101–118.

    Article  Google Scholar 

  • Roberts, J., & Styron, R. (2010). Student satisfaction and persistence: Factors vital to student retention. Research in Higher Education Journal, 6, 1–18.

    Google Scholar 

  • Rosenbaum, P. R. (1991). A characterization of optimal designs for observational studies. Journal of the Royal Statistical Society. Series B (Methodological), 53(3), 597–610.

    Google Scholar 

  • Rosenbaum, P. R. (2002). Observational studies. New York: Springer.

    Book  Google Scholar 

  • Rosenbaum, P. R. (2005). Sensitivity analysis in observational studies. Encyclopedia of Statistics in Behavioral Sciences, 4, 1809–1814.

    Google Scholar 

  • Rosenbaum, P. R. (2010). Design of observational studies. Philadelphia: Springer.

    Book  Google Scholar 

  • Rosenbaum, P. R., & Rubin, R. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, R. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79(387), 516–524.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39(1), 33–38.

    Google Scholar 

  • Roth, P. (1994). Missing data: A conceptual review for applied psychology. Personnel Psychology, 47(3), 537–560.

    Article  Google Scholar 

  • Rubin, D. (1974). Estimating causal effects of treatments in randomised and nonrandomised studies. Journal of Educational Psychology, 66, 688–701.

    Article  Google Scholar 

  • Rubin, D. B. (2002). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services and Outcomes Research Methodology, 2, 169–188.

    Article  Google Scholar 

  • Rubin, D. B. (2004). Teaching statistical inference for causal effects in experiments and observational studies. Journal of Educational and Behavioral Statistics, 29(3), 343–367.

    Article  Google Scholar 

  • Rubin, D. B., & Thomas, N. (1996). Matching using estimated propensity scores: Relating theory to practice. Biometrics, 52(1), 249–264.

    Article  Google Scholar 

  • Schneider, B., Carnoy, M., Kilpatrick, J., & Shavelson, R. J. (2007). Estimating causal effects using experimental and observational designs. Washington, DC: American Educational Research Association.

  • Seidman, A. (1991). The evaluation of a pre/post admissions/counseling process at a suburban community college: Impact on student satisfaction with the faculty and the institution, retention, and academic performance. College and University, 66, 223–232.

    Google Scholar 

  • Sianesi, B. (2004). An evaluation of the Swedish system of active labor market programs in the 1990s. Review of Economics and Statistics, 86(1), 133–155.

    Article  Google Scholar 

  • Smith, J. B., Walter, T. L., & Hoey, G. (1992). Support programs and student self-efficacy: Do first-year students know when they need help? Journal of the Freshman Year Experience, 4(2), 41–67.

    Google Scholar 

  • Snyder, T. D., & Dillow, S. A. (2011). Digest of education statistics 2010. Washington, DC: US Department of Education.

    Google Scholar 

  • Söderbom, M. (2009). Applied econometrics, lecture 11: Treatment effects part 1. University of Gothenburgy Department of Economics lecture notes. http://www.soderbom.net/lecture11_notes.pdf. Accessed November 28, 2012.

  • Steingass, S. J., & Sykes, S. (2008). Centralizing advising to improve student outcomes. Peer Review, 10(1), 18–20.

    Google Scholar 

  • Szafran, R. F. (2001). The effect of academic load on success for new college students: Is lighter better? Research in Higher Education, 42(1), 27–50.

    Article  Google Scholar 

  • Thalheimer, W., & Cook, S. (2002). How to calculate effect sizes from published research articles: A simplified methodology. A Working-Learning Research publication. http://www.bwgriffin.com/gsu/courses/edur9131/content/Effect_Sizes_pdf5.pdf. Accessed September 12, 2012.

  • Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125.

    Article  Google Scholar 

  • Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: The University of Chicago Press.

    Google Scholar 

  • Tinto, V. (1999). Taking retention seriously: Rethinking the first year of college. NACADA Journal, 19(12), 5–9.

    Article  Google Scholar 

  • Titus, M. (2007). Detecting selection bias, using propensity score matching, and estimating treatment effects: An application to the private returns to a master’s degree. Research in Higher Education, 48(4), 487–521.

    Article  Google Scholar 

  • Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, Mass: MIT Press.

    Google Scholar 

  • Wooldridge, J. M. (2009). Introductory econometrics: A modern approach. Mason, Ohio: South-Western Cengage Learning.

    Google Scholar 

Download references

Acknowledgments

I thank the Student Advisement Center and the Office of institutional Research at the study institution for their collaborative effort to track undergraduate students’ use of academic advising and for facilitating access to the data. I thank Mr. Erik J. Lauffer for his assistance with the data and Dr. Peter Lyons and Dr. Teresa E. Ward for their feedback on an earlier version of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Felly Chiteng Kot.

Appendices

Appendix 1

See Table 10.

Table 10 Covariate imbalance before and after matching

Appendix 2

See Figs. 2, 3 and 4.

Fig. 2
figure 2

Distribution of estimated propensity score: first-term GPA and first-year cumulative GPA

Fig. 3
figure 3

Distribution of estimated propensity score: second-term GPA

Fig. 4
figure 4

Distribution of estimated propensity score: second-year enrollment behavior

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chiteng Kot, F. The Impact of Centralized Advising on First-Year Academic Performance and Second-Year Enrollment Behavior. Res High Educ 55, 527–563 (2014). https://doi.org/10.1007/s11162-013-9325-4

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11162-013-9325-4

Keywords

Navigation