Research in Higher Education

, Volume 59, Issue 3, pp 249–272 | Cite as

When Do Honors Programs Make the Grade? Conditional Effects on College Satisfaction, Achievement, Retention, and Graduation

  • Nicholas A. Bowman
  • KC Culver


Many people within and outside of higher education view honors programs as providing meaningful academic experiences that promote learning and growth for high-achieving students. To date, the research exploring the link between honors participation and college grades and retention has obtained mixed results; some of the seemingly conflicting findings may stem from the presence of methodological limitations, including the difficulty with adequately accounting for selection into honors programs. In addition, virtually no research has explored the conditions under which honors programs are most strongly related to desired outcomes. To provide a rigorous examination of the potential impact of this experience, this study conducted propensity score analyses with a large, multi-institutional, longitudinal sample of undergraduates at 4-year institutions. In the full sample, honors participation predicts greater college GPA and 4-year graduation, while it is unrelated to college satisfaction and retention. However, these results differ notably by institutional selectivity: Honors participation is associated with greater college GPA, retention to the third and fourth years of college, and 4-year graduation at less selective institutions, but it is significantly related only to GPA at more selective institutions. These relationships are also sometimes larger among students from historically underrepresented groups.


Honors programs College honors College satisfaction Academic achievement Retention Graduation Institutional selectivity 



This research was supported by a grant from the Center of Inquiry in the Liberal Arts at Wabash College to the Center for Research on Undergraduate Education at the University of Iowa.


  1. Achterberg, C. (2005). What is an honors student? Journal of the National Collegiate Honors Council, 6(1), 75–83.Google Scholar
  2. Akers, A. (2010). Determination of the optimal number of strata for bias reduction in propensity score matching. Dissertation Abstracts International, 71(08), 58A. (UMI No. 3417726)Google Scholar
  3. An, B. P., Loes, C. N., & Trolian, T. L. (in press). Binge drinking on academic performance: Considering mediating effects of academic involvement. Journal of College Student Development.Google Scholar
  4. Astin, A. W. (1970). The methodology of research on college impact, part one. Sociology of Education, 43(3), 223–254.CrossRefGoogle Scholar
  5. Astin, A. W. (1984). Student involvement: A developmental theory for higher education. Journal of College Student Personnel, 25(4), 297–308.Google Scholar
  6. Astin, A. W. (1993). What matters in college? Four critical years revisited. San Francisco: Jossey-Bass.Google Scholar
  7. Austin, C. G. (1991). Honors programs: Development, review, and revitalization. Charleston: National Collegiate Honors Council.Google Scholar
  8. Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46, 399–424.CrossRefGoogle Scholar
  9. Bailey, T., Jaggars, S. S., & Jenkins, D. (2015). Redesigning America’s community colleges: A clearer path to student success. Cambridge: Harvard University Press.CrossRefGoogle Scholar
  10. Bean, J., & Eaton, S. (2000). A psychological model of college student retention. In J. M. Braxton (Ed.), Reworking the student departure puzzle (pp. 48–61). Nashville: Vanderbilt University Press.Google Scholar
  11. Berger, J. B., & Milem, J. F. (2000). Organizational behavior in higher education and student outcomes. In J. Smart (Ed.), Higher education: Handbook of theory and research (pp. 268–338). New York: Agathon Press.Google Scholar
  12. Biemer, P. P., & Christ, S. L. (2008). Weighting survey data. In E. D. de Leeuw, J. J. Hox, & D. A. Dillman (Eds.), International handbook of survey methodology (pp. 317–341). New York: Psychology Press.Google Scholar
  13. Bowman, N. A., & Culver, K. (in press). Promoting equity and student learning: Rigor in undergraduate academic experiences. In C. M. Campbell (Ed.), Reframing notions of rigor: Building scaffolding for equity and student success (New Directions for Higher Education). San Francisco, CA: Jossey-Bass.Google Scholar
  14. Bowman, N. A., Denson, N., & Park, J. J. (2016). Racial/cultural awareness workshops and post-college civic engagement: A propensity score matching approach. American Educational Research Journal, 53(6), 1556–1587.CrossRefGoogle Scholar
  15. Bowman, N. A., Park, J. J., & Denson, N. (2015). Student involvement in ethnic student organizations: Examining civic outcomes six years after graduation. Research in Higher Education, 56(2), 127–145.CrossRefGoogle Scholar
  16. Braxton, J. M., Hirschy, A. S., & McClendon, S. A. (2004). Understanding and reducing college student departure (ASHE-ERIC Higher Education Report 30-3). San Francisco: Jossey-Bass.Google Scholar
  17. 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.CrossRefGoogle Scholar
  18. Cabin, R. J., & Mitchell, R. J. (2000). To Bonferroni or not to Bonferroni: When and how are the questions. Bulletin of the Ecological Society of America, 81(3), 246–248.Google Scholar
  19. Cabrera, A. F., Nora, A., & Castañeda, M. B. (1992). The role of finances in the persistence process: A structural model. Research in Higher Education, 33, 571–593.CrossRefGoogle Scholar
  20. Cacioppo, J., Petty, R., Feinstein, J., & Jarvis, W. (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in need for cognition. Psychological Bulletin, 119, 197–253.CrossRefGoogle Scholar
  21. Chen, R., & DesJardins, S. L. (2010). Investigating the impact of financial aid on student dropout risks: Racial and ethnic differences. Journal of Higher Education, 81(2), 179–208.CrossRefGoogle Scholar
  22. Cochran, W. G. (1968). The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics, 24, 295–313.CrossRefGoogle Scholar
  23. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah: Lawrence Erlbaum.Google Scholar
  24. Cosgrove, J. R. (2004). The impact of honors programs on undergraduate academic performance, retention, and graduation. Journal of the National Collegiate Honors Council, 5(2), 45–53.Google Scholar
  25. Cruce, T. M. (2009). A note on the calculation and interpretation of the delta-p statistic for categorical independent variables. Research in Higher Education, 50(6), 608–622.CrossRefGoogle Scholar
  26. Cruce, T. M., Wolniak, G. C., Seifert, T. A., & Pascarella, E. T. (2006). Impacts of good practices on cognitive development, learning orientations, and graduate degree plans during the first year of college. Journal of College Student Development, 47(4), 365–383.CrossRefGoogle Scholar
  27. Digby, J. (2005). Peterson’s smart choices: Honors programs and colleges (4th ed.). Lawrenceville: Thomson Peterson’s.Google Scholar
  28. Eckles, J. E., & Stradley, E. G. (2012). A social network analysis of student retention using archival data. Social Psychology of Education, 15, 165–180.CrossRefGoogle Scholar
  29. Farrington, C. A., Roderick, M., Allensworth, E., Nagaoka, J., Keyes, T. S., Johnson, D. W., et al. (2012). Teaching adolescents to become learners. The role of noncognitive factors in shaping school performance: A critical literature review. Chicago: University of Chicago Consortium on Chicago School Research.Google Scholar
  30. Graunke, S. S., & Woosley, S. A. (2005). An exploration of the factors that affect the academic success of college sophomores. College Student Journal, 39(2), 367–376.Google Scholar
  31. Griswald, M. E., Localio, A. R., & Mulrow, C. (2010). Propensity score adjustment with multilevel data: Setting your sites on decreasing selection bias. Annals of Internal Medicine, 152(6), 393–395.CrossRefGoogle Scholar
  32. Groves, R. M., Fowler, F. J., Jr., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey methodology (2nd ed.). Hoboken: Wiley.Google Scholar
  33. Guo, S., & Fraser, M. W. (2015). Propensity score analysis: Statistical methods and applications (2nd ed.). Los Angeles: Sage.Google Scholar
  34. Haak, D. C., Hille Ris Lambers, J., Pitre, E., & Freeman, S. (2011). Increased structure and active learning reduce the achievement gap in introductory biology. Science, 332(6034), 1213–1216.CrossRefGoogle Scholar
  35. Hartleroad, G. E. (2005). Comparison of the academic achievement of first year female honors program and non-honors program engineering students. Journal of the National Collegiate Honors Council, 6(2), 109–120.Google Scholar
  36. Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York: Routledge.Google Scholar
  37. Heck, R. H., & Thomas, S. L. (2009). An introduction to multilevel modeling techniques (2nd ed.). New York: Routledge.Google Scholar
  38. Herzog, S. (2011). Gauging academic growth of bachelor degree recipients: Longitudinal vs. self-reported gains in general education. In S. Herzog & N. A. Bowman (Eds.), Validity and limitations of college student self-report data (New Directions for Institutional Research, No. 150, pp. 113–120). San Francisco: Jossey-Bass.Google Scholar
  39. Holmes, W. M. (2013). Using propensity scores in quasi-experimental designs. Los Angeles: Sage.Google Scholar
  40. Hong, G., & Raudenbush, S. W. (2005). Effects of kindergarten retention policy on children’s cognitive growth in reading and mathematics. Educational Evaluation and Policy Analysis, 27, 205–224.CrossRefGoogle Scholar
  41. Hong, G., & Raudenbush, S. W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multilevel observational data. Journal of the American Statistical Association, 101, 901–910.CrossRefGoogle Scholar
  42. Ishitani, T. T. (2008). How do transfers survive after “transfer shock”? A longitudinal study of transfer student departure. Research in Higher Education, 49(5), 403–419.CrossRefGoogle Scholar
  43. Jaccard, J., & Turrisi, R. (2003). Interaction effects in multiple regression (2nd ed.). Newbury Park: Sage.CrossRefGoogle Scholar
  44. Jaeger, A. J., & Eagan, M. K., Jr. (2011). Examining retention and contingent faculty use in a state system of public higher education. Educational Policy, 25(3), 507–537.CrossRefGoogle Scholar
  45. Johnson, V. E. (2003). Grade inflation: A crisis in higher education. New York: Springer.Google Scholar
  46. Keller, R. R., & Lacy, M. G. (2013). Propensity score analysis of an honors program’s contribution to students’ retention and graduation outcomes. Journal of the National Collegiate Honors Council, 2, 73–84.Google Scholar
  47. Kim, M. M. (2002). Cultivating intellectual development: Comparing women-only colleges and coeducational colleges for educational effectiveness. Research in Higher Education, 43(4), 447–481.CrossRefGoogle Scholar
  48. Kinzie, J., Gonyea, R., Kuh, G. D., Umbach, P. D., Blaich, C., & Korkmaz, A. (2007, November). The relationship between gender and student engagement in college. Paper presented at the annual meeting of the Association for the Study of Higher Education, Louisville, KY.Google Scholar
  49. Kuh, G. D., Cruce, T. M., Shoup, R., & Kinzie, J. (2008). Unmasking the effects of student engagement on first-year college grades and persistence. Journal of Higher Education, 79(5), 540–563.CrossRefGoogle Scholar
  50. Li, D. (2010). They need help: Transfer students from four-year to four-year institutions. Review of Higher Education, 33(2), 207–238.Google Scholar
  51. Mayhew, M. J., Rockenbach, A. N., Bowman, N. A., Seifert, T. A., Wolniak, G. C., Pascarella, E. T., et al. (2016). How college affects students (Vol. 3): 21st century evidence that higher education works. San Francisco: Jossey-Bass.Google Scholar
  52. Melguizo, T. (2011). A review of the theories developed to describe the process of college persistence and attainment. In J. C. Smart & M. B. Paulsen (Eds.), Higher education: Handbook of theory and research (Vol. 26, pp. 395–424). New York: Springer.CrossRefGoogle Scholar
  53. Moon, J. L. (2012). Honors and high-ability students: Factors that predict academic efficacy, critical thinking skills, and academic goals. Doctoral dissertation, Available from Proquest Dissertations and Theses database (UMI No. 3511628)Google Scholar
  54. Museus, S. D. (2014). The culturally engaging campus environments (CECE) model: A new theory of college success among racially diverse student populations. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (pp. 189–227). New York: Springer.CrossRefGoogle Scholar
  55. National Collegiate Honors Council. (2017). About NCHC. Retrieved from
  56. Ogilvie, K., & Reza, E. M. (2009). Business student performance in traditional vs. honors course settings. Business Education Innovation Journal, 1(2), 31–37.Google Scholar
  57. Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124(1), 54–74.CrossRefGoogle Scholar
  58. Pace, C. R. (1982). Achievement and the quality of student effort. Washington, DC: U.S. Department of Education.Google Scholar
  59. Pan, W., & Bai, H. (Eds.). (2015). Propensity score analysis: Fundamentals and developments. New York: Guilford Press.Google Scholar
  60. Park, D. C. & Maisto, A. A. (1984). Assessment of the impact of an introductory honors psychology course on students: Initial and delayed effects. Annual Meeting of the Southeastern Psychological Association (p. 14).Google Scholar
  61. Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students (Vol. 2): A third decade of research. San Francisco, CA: Jossey-Bass.Google Scholar
  62. Patrick, A. R., Schneeweiss, S., Brookhart, M. A., Glynn, R. J., Rothman, K. J., Avorn, J., et al. (2011). The implications of propensity score variable selection strategies in pharmacoepidemiology: An empirical illustration. Pharmacoepidemiology and Drug Safety, 20(6), 551–559.CrossRefGoogle Scholar
  63. Pflaum, S. W., Pascarella, E. T., & Duby, P. (1985). The effects of honors college participation on academic performance during the freshman year. Journal of College Student Personnel, 26(5), 414–419.Google Scholar
  64. Preszler, R. W. (2009). Replacing lecture with peer-led workshops improves student learning. CBE—Life Sciences Education, 8(3), 182–192.CrossRefGoogle Scholar
  65. Radford, A. W., Berkner, L., Wheeless, S. C., & Shepherd, B. (2010). Persistence and attainment of 2003–04 beginning postsecondary students: After 6 years (NCES 2011-151). Washington, DC: U.S. Department of Education.Google Scholar
  66. Raley, R. K., Kim, Y., & Daniels, K. (2012). Young adults’ fertility expectations and events: Associations with college enrollment and persistence. Journal of Marriage & Family, 74(4), 866–879.CrossRefGoogle Scholar
  67. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park: Sage.Google Scholar
  68. Rice, W. R. (1989). Analyzing tables of statistical tests. Evolution, 43(1), 223–225.CrossRefGoogle Scholar
  69. Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138, 353–387.CrossRefGoogle Scholar
  70. Rinn, A. N. (2007). Effects of programmatic selectivity on the academic achievement, academic self-concepts, and aspirations of gifted college students. Gifted Child Quarterly, 51(3), 232–245.CrossRefGoogle Scholar
  71. Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological Bulletin, 130, 261–288.CrossRefGoogle Scholar
  72. Roksa, J. (2011). Differentiation and work: Inequality in degree attainment in U.S. higher education. Higher Education, 61(3), 293–308.CrossRefGoogle Scholar
  73. Roksa, J., & Keith, B. (2008). Credits, time, and attainment: Articulation policies and success after transfer. Educational Evaluation and Policy Analysis, 30(3), 236–254.CrossRefGoogle Scholar
  74. Roszkowski, M. J., & Nigro, R. A. (2015). The value of SAT scores and high school grades in the selection of honors program candidates from the perspective of honors students and graduates. Strategic Enrollment Management Quarterly, 2(4), 259–293.Google Scholar
  75. Ryff, C. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57, 1069–1081.CrossRefGoogle Scholar
  76. Seifert, T. A., Pascarella, E. T., Colangelo, N., & Assouline, S. (2007). The effects of honors program participation on experiences of good practices and learning outcomes. Journal of College Student Development, 48(1), 57–74.CrossRefGoogle Scholar
  77. Shushok, F. (2002). Educating the best and the brightest: Collegiate honors programs and the intellectual, social and psychological development of students. Doctoral dissertation. Available from Proquest Dissertations and Theses database (UMI No. 3070562).Google Scholar
  78. Shushok, F. J. (2006). Student outcomes and honors programs: A longitudinal study of 172 honors students 2000–2004. Journal of the National Collegiate Honors Council, 7(2), 85–96.Google Scholar
  79. Slavin, C., Coladarci, T., & Pratt, P. A. (2008). Is student participation in a honors program related to retention and graduation rates? Journal of the National Collegiate Honors Council, 9(2), 59–69.Google Scholar
  80. Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
  81. Somers, P., Woodhouse, S., & Cofer, J. (2004). Pushing the boulder uphill: The persistence of first-generation college students. NASPA Journal, 41(3), 418–435.CrossRefGoogle Scholar
  82. St John, E. P., Hu, S., Simmons, A., Carter, D. F., & Weber, J. (2004). What difference does a major make? The influence of college major field on persistence by African American and White students. Research in Higher Education, 45(3), 209–232.CrossRefGoogle Scholar
  83. Steiner, P. M., Cook, T. D., Li, W., & Clark, M. H. (2015). Bias reduction in quasi-experiments with little selection theory but many covariates. Journal of Research on Educational Effectiveness, 8, 552–576.CrossRefGoogle Scholar
  84. Strayhorn, T. L. (2012). College students’ sense of belonging: A key to educational success for all students. New York: Routledge.Google Scholar
  85. Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: University of Chicago Press.Google Scholar
  86. Vaughan, A. L., Lalonde, T. L., & Jenkins-Guarnieri, M. A. (2014). Assessing student achievement in large-scale educational programs using hierarchical propensity scores. Research in Higher Education, 55, 564–580.CrossRefGoogle Scholar
  87. Wang, Q. (2015). Propensity score matching on multilevel data. In W. Pan & H. Bai (Eds.), Propensity score analysis: Fundamentals and developments (pp. 217–235). New York: Guilford.Google Scholar
  88. Westreich, D., Cole, S. R., Funk, M. J., Brookhart, M. A., & Stürmer, T. (2011). The role of the c-statistic in variable selection for propensity score models. Pharmacoepidemiology and Drug Safety, 20(3), 317–320.CrossRefGoogle Scholar
  89. Wolgemuth, A., Whalen, D., Sullivan, J., Nading, C., Shelley, M., & Wang, Y. (2007). Financial, academic and environmental influences on the retention and graduation of students. Journal of College Student Retention, 8(4), 457–475.CrossRefGoogle Scholar
  90. Xiang, Y., & Wang, S. (2013). An application of propensity score stratification using multilevel models: Do charter schools make a difference in student achievement and growth?. Portland: Northwest Evaluation Association.Google Scholar
  91. Yeager, D. S., & Walton, G. M. (2011). Social-psychological interventions in education: They’re not magic. Review of Educational Research, 81, 267–301.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.N491 Lindquist CenterUniversity of IowaIowa CityUSA

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