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

Research in Higher Education

Volumn 55, Issue 6, 2014, Pages 527-563

  Chiteng Kot, Felly ^a  

^a GEORGIA STATE UNIVERSITY   (United States)

Author keywords

Academic advising; Academic performance; Credit load; Propensity score matching; Retention; Undergraduate GPA

Indexed keywords

EID: 84905724438     PISSN: 03610365     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11162-013-9325-4     Document Type: Article

References (94)

1. Adelman, C. (1999). Answers in the toolbox: Academic intensity, attendance patterns, and bachelor's degree attainment. Washington, DC: U. S. Department of Education.
2. 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.
3. Astin, A. W. (1970a). The methodology of research on college impact, part one. Sociology of Education, 43(3), 223-254.
4. Astin, A. W. (1970b). The methodology of research on college impact, part two. Sociology of Education, 43(4), 437-450.
5. Astin, A. W. (1991). Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education. New York: MacMillan.
6. 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.
7. 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.
8. 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.
9. 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.
10. Baum, S., & Payea, K. (2005). Education pays 2004: The benefits of higher education for individuals and society. Washington, DC: The College Board.
11. Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55(4), 485-540.
12. Beatty, J. D. (1991). The National Academic Advising Association: A brief narrative history. NACADA Journal, 11(1), 5-25.
13. Becker, S. O., & Caliendo, M. (2007). Sensitivity analysis for average treatment effects. The Stata Journal, 7(1), 71-83.
14. Board, College. (2010). Trends in college pricing. Washington, DC: The College Board.
15. 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.
16. 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.
17. 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.
18. College Board. (2009). How colleges organize themselves to increase student persistence: Four-year institutions. New York: College Board.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
25. Ensign, R. L. (2010). Fast gainers: 4 ways that colleges have raised graduation rates. Chronicle of Higher Education, 57(16), A15.
26. GAO. (2012). Postsecondary education: Financial trends in public and private nonprofit institutions. Washington, DC: United States Government Accountability Office.
27. 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.
28. 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.
29. 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.
30. Guo, S., & Fraser, M. W. (2010). Propensity score analysis: Statistical methods and applications. Los Angeles: Sage.
31. Habley, W. R. (1983). Organizational structures for academic advising: Models and implications. Journal of College Student Personnel, 24(6), 535-540.
32. Habley, W. R. (2003). Faculty advising: Practice and promise. In G. L. Kramer (Ed.), Faculty advising: Practice and promise (pp. 23-39). Boston: Anker.
33. 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.
34. Habley, W. R., Bloom, J. L., & Robbins, S. (2012). Increasing persistence: Research-based strategies for college student success. San Francisco: Jossey-Bass.
35. 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.
36. Hester, E. J. (2008). Student evaluations of advising: Moving beyond the mean. College Teaching, 56(1), 35-38.
37. 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.
38. 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.
39. Imai, K., King, G., & Lau, O. (2007). Zelig: Everyone's statistical software. http://gking. harvard. edu/zelig/docs/zelig. pdf. Accessed August 31, 2012.
40. 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.
41. 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.
42. Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, 86(1), 4-29.
43. 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.
44. Kelderman, E. (2012). With state support now tied to completion, Tennessee colleges must refocus. Chronicle of Higher Education, 59(6), A16-A18.
45. King, M. C. (1993). Academic advising, retention, and transfer. New Directions for Community Colleges, 82, 21-31.
46. 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.
47. Light, R. J. (2001). Making the most of college: Students speak their minds. Cambridge, MA: Harvard University Press.
48. Long, S. J. (1997). Regression models of categorical and limited dependent variables. Thousand Oaks: Sage.
49. 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.
50. McPherson, M. S., & Schapiro, M. O. (Eds.). (2008). College success: What it means and how to make it happen. New York: The College Board.
51. 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.
52. Metzner, B. S. (1989). Perceived quality of academic advising: The effect on freshman attrition. American Educational Research Journal, 26(3), 422-442.
53. 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.
54. 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.
55. Murnane, R. J., & Willett, J. B. (2010). Methods matter: Improving causal inference in educational and social science research. New York: Oxford University Press.
56. Murtaugh, P. A., Burns, L. D., & Schuster, J. (1999). Predicting the retention of university students. Research in Higher Education, 40(3), 355-371.
57. 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.
58. OECD. (2011). Education at a glance 2011: OECD indicators. Paris: OECD.
59. 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.
60. 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.
61. 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.
62. Pascarella, E. T., & Terenzini, P. T. (2005). How college affect students: A third decade of research. San Francisco: Jossey-Bass.
63. Pietras, S. A. (2010). The impact of academic advising on GPA and retention at the community college level. Doctoral dissertation, Indiana University of Pennsylvania,.
64. 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.
65. 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.
66. 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.
67. Roberts, J., & Styron, R. (2010). Student satisfaction and persistence: Factors vital to student retention. Research in Higher Education Journal, 6, 1-18.
68. 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.
69. Rosenbaum, P. R. (2002). Observational studies. New York: Springer.
70. Rosenbaum, P. R. (2005). Sensitivity analysis in observational studies. Encyclopedia of Statistics in Behavioral Sciences, 4, 1809-1814.
71. Rosenbaum, P. R. (2010). Design of observational studies. Philadelphia: Springer.
72. 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.
73. 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.
74. 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.
75. Roth, P. (1994). Missing data: A conceptual review for applied psychology. Personnel Psychology, 47(3), 537-560.
76. Rubin, D. (1974). Estimating causal effects of treatments in randomised and nonrandomised studies. Journal of Educational Psychology, 66, 688-701.
77. 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.
78. 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.
79. Rubin, D. B., & Thomas, N. (1996). Matching using estimated propensity scores: Relating theory to practice. Biometrics, 52(1), 249-264.
80. Schneider, B., Carnoy, M., Kilpatrick, J., & Shavelson, R. J. (2007). Estimating causal effects using experimental and observational designs. Washington, DC: American Educational Research Association.
81. 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.
82. 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.
83. 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.
84. Snyder, T. D., & Dillow, S. A. (2011). Digest of education statistics 2010. Washington, DC: US Department of Education.
85. 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.
86. Steingass, S. J., & Sykes, S. (2008). Centralizing advising to improve student outcomes. Peer Review, 10(1), 18-20.
87. 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.
88. 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.
89. Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89-125.
90. Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: The University of Chicago Press.
91. Tinto, V. (1999). Taking retention seriously: Rethinking the first year of college. NACADA Journal, 19(12), 5-9.
92. 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.
93. Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, Mass: MIT Press.
94. Wooldridge, J. M. (2009). Introductory econometrics: A modern approach. Mason, Ohio: South-Western Cengage Learning.