Research in Higher Education

, Volume 51, Issue 8, pp 724–749 | Cite as

The Bird’s Eye View of Community Colleges: A Behavioral Typology of First-Time Students Based on Cluster Analytic Classification

Article

Abstract

The development of a typology of community college students is a topic of long-standing and growing interest among educational researchers, policy-makers, administrators, and other stakeholders, but prior work on this topic has been limited in a number of important ways. In this paper, I develop a behavioral typology based on students’ course-taking and other enrollment patterns during a seven-year observation period. Drawing on data for a population of 165,921 first-time college students, I identify six clusters of behaviors: transfer, vocational, drop-in, noncredit, experimental, and exploratory. I describe each of these student types in terms of distinguishing course-taking and enrollment behaviors, representation in the first-time student cohort, predominant demographic characteristics, and self-reported academic goal. I test the predictive validity of the classification scheme with respect to long-term academic outcomes. I investigate the relationships between the primary classification scheme and several alternative classification schemes. Finally, I demonstrate the replicability of the classification scheme with an alternate cohort of students.

Keywords

Community college Student Taxonomy Typology Classification Cluster analysis Transfer Persistence Retention Experimental Exploratory Vocational Noncredit 

References

  1. Adelman, C. (2005a). Moving into town—and moving on: The community college in the lives of traditional-age students. Washington, DC: U.S. Department of Education.Google Scholar
  2. Adelman, C. (2005b). Educational ‘anticipations’ of traditional age community college students: A prolegomena of any future accountability indicators. Journal of Applied Research in the Community College, 12, 93–107.Google Scholar
  3. Ammon, B. V., Bowman, J., & Mourad, R. (2008). Who are our students?: Cluster analysis as a tool for understanding community college student populations. Journal of Applied Research in the Community College, 16, 32–44.Google Scholar
  4. Astin, A. W. (1993). An empirical typology of college students. Journal of College Student Development, 34, 36–46.Google Scholar
  5. Attinasi, L. C., Stahl, V. V., & Okun, M. A. (1982). A preliminary typology of motivational orientations of community college students. Community/Junior College Quarterly, 6, 371–390.Google Scholar
  6. Bahr, P. R. (2007). Double jeopardy: Testing the effects of multiple basic skill deficiencies on successful remediation. Research in Higher Education, 48, 695–725.CrossRefGoogle Scholar
  7. Bahr, P. R. (2008). Does mathematics remediation work?: A comparative analysis of academic attainment among community college students. Research in Higher Education, 49, 420–450.CrossRefGoogle Scholar
  8. Bahr, P. R. (2009a). Educational attainment as process: Using hierarchical discrete-time event history analysis to model rate of progress. Research in Higher Education, 50, 691–714.CrossRefGoogle Scholar
  9. Bahr, P. R. (2009b). Classifying California’s community college students: A technical report. Sacramento, CA: Chancellor’s Office of the California Community Colleges.Google Scholar
  10. Bahr, P. R. (2009c). College hopping: Exploring the occurrence, frequency, and consequences of lateral transfer. Community College Review, 36, 271–298.CrossRefGoogle Scholar
  11. Bahr, P. R. (2010a). Revisiting the efficacy of postsecondary remediation: The moderating effects of depth/breadth of deficiency. Review of Higher Education, 33, 177–205.CrossRefGoogle Scholar
  12. Bahr, P. R. (2010b). Making sense of disparities in mathematics remediation: What is the role of student retention? Journal of College Student Retention, 12, 25–49.CrossRefGoogle Scholar
  13. Bahr, P. R. (2010c). Preparing the underprepared. An analysis of racial disparities in postsecondary mathematics remediation. Journal of Higher Education, 81, 209–237.CrossRefGoogle Scholar
  14. Bahr, P. R., Hom, W., & Perry, P. (2004). Student readiness for postsecondary coursework: Developing a college-leave measure of student average academic preparation. Journal of Applied Research in the Community College, 12, 7–16.Google Scholar
  15. Bahr, P. R., Hom, W., & Perry, P. (2005). College transfer performance: A methodology for equitable measurement and comparison. Journal of Applied Research in the Community College, 13, 73–87.Google Scholar
  16. Bailey, T. R., & Averianova, I. E. (1998). Multiple missions of community colleges: Conflicting or complementary? New York: Community College Research Center. ERIC Document Reproduction Service No. ED439762.Google Scholar
  17. Borden, V. M. H. (1995). Segmenting student markets with a student satisfaction and priorities survey. Research in Higher Education, 36, 73–88.CrossRefGoogle Scholar
  18. Borden, V. M. H. (2005). Identifying and analyzing group differences. In M. A. Coughlin (Ed.), Intermediate/advanced statistics in institutional research (pp. 132–168). Tallahassee, FL: Association for Institutional Research.Google Scholar
  19. Boughan, K. (2000). The role of academic process in student achievement: An application of structural equation modeling and cluster analysis to community college longitudinal data. AIR Professional File, 74, 1–18.Google Scholar
  20. Bradburn, E. M., & Hurst, D. G. (2001). Community college transfer rates to 4-year institutions using alternative definitions of transfer (NCES 2001–197). Washington, DC: National Center for Education Statistics.Google Scholar
  21. Capps, R. (n.d.). Adult learners’ persistence at a community college. Paper presented November 7, 2009, at the annual meeting of the association for the study of higher education, Vancouver, BC, Canada.Google Scholar
  22. Chancellor’s Office of the California Community Colleges. (2004). Taxonomy of programs (6th ed.). Sacramento, CA: California Community Colleges, Chancellor’s Office.Google Scholar
  23. Cohen, A. M., Brawer, F. B., & Lombardi, J. R. (2008). The American community college student. San Francisco, CA: Jossey-Bass.Google Scholar
  24. Cormack, R. M. (1971). A review of classification. Journal of the Royal Statistical Society, Series A, 134, 321–367.CrossRefGoogle Scholar
  25. Darwin, C. (1859). On the origin of species by means of natural selection. London: John Murray.Google Scholar
  26. Deil-Amen, R., & Rosenbaum, J. E. (2004). Charter building and labor market outcomes in two-year colleges. Sociology of Education, 77, 245–265.CrossRefGoogle Scholar
  27. Dellow, D. A., & Romano, R. M. (2002). Measuring outcomes: Is the first-time, full-time cohort appropriate for the community college? Community College Review, 30, 42–54.CrossRefGoogle Scholar
  28. Despite budget woes, Arizona colleges reject tuition hike. (2009). Community College Week, 21(17), 3–4.Google Scholar
  29. Dougherty, K. J., & Hong, E. (2006). Performance accountability as imperfect panacea: The community college experience. In T. Bailey & V. S. Morest (Eds.), Defending the community college equity agenda (pp. 51–86). Baltimore: John Hopkins University Press.Google Scholar
  30. Dowd, A. C. (2003). From access to outcome equity: Revitalizing the democratic mission of the community college. Annals of the American Academic of Political and Social Science, 586, 92–119.CrossRefGoogle Scholar
  31. Dowd, A. C., & Tong, V. P. (2007). Accountability, assessment, and the scholarship of “best practice”. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. XXII, pp. 57–119). New York: Springer.CrossRefGoogle Scholar
  32. Firebaugh, G. (2008). Seven rules of social research. Princeton, NJ: Princeton University Press.Google Scholar
  33. Fisher, M. (2009). Calif Budget cuts battering 2-year colleges. Community College Week, 21(10), 5.Google Scholar
  34. Gillmore, G. M., & Hoffman, P. H. (1997). The graduate efficiency index: Validity and use as an accountability and research measure. Research in Higher Education, 38, 677–697.CrossRefGoogle Scholar
  35. Goldrick-Rab, S. (2009, May 15). American must put community colleges first. Chronicle of Higher Education, 55(36), A99. Retrieved May 29, 2009, from http://chronicle.com/weekly/v55/i36/36a09901.htm.
  36. Gottfredson, D. M. (1987). Prediction and classification in criminal justice decision-making. Crime and Justice, 9, 1–20.CrossRefGoogle Scholar
  37. Hagedorn, L. S., & Kress, A. M. (2008). Using transcripts in analyses: Directions and opportunities. New Directions for Community Colleges, 143, 7–17.CrossRefGoogle Scholar
  38. Hagedorn, L. S., & Prather, G. (2005). The community college solar system: If university students are from Venus community college students must be from Mars. Paper presented at the 2005 Annual Forum of the Association for Institutional Research, San Diego, California.Google Scholar
  39. Hewitt, J. P. (2007). Self and society: A symbolic interactionist social psychology. Boston, MA: Allyn and Bacon.Google Scholar
  40. Hogg, M. A., Terry, D. J., & White, K. M. (1995). A tale of two theories: A critical comparison of identity theory with social identity theory. Social Psychology Quarterly, 58, 255–269.CrossRefGoogle Scholar
  41. Hom, W. C. (2009). The denominator as the ‘target’. Community College Review, 37, 136–152.CrossRefGoogle Scholar
  42. Horn, L. (2009). On track to complete? A taxonomy of beginning community college students and their outcomes 3 years after enrolling: 200304 through 2006 (NCES 2009-152). Washington, DC.: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.Google Scholar
  43. Inkelas, K. K., Soldner, M., Longerbeam, S. D., & Leonard, J. B. (2008). Differences in student outcomes by types of living-learning programs: The development of an empirical typology. Research in Higher Education, 49, 495–512.CrossRefGoogle Scholar
  44. Iowa Senate approves cuts in higher education. (2009). Community College Week, 21(18), 10.Google Scholar
  45. Kane, T. J., & Rouse, C. E. (1999). The community college: Educating students at the margin between college and work. Journal of Economic Perspectives, 13, 63–84.CrossRefGoogle Scholar
  46. Keller, J. (2009a, May 21). California community colleges may reduce enrollment by 250,000 students. Chronicle of Higher Education. Retrieved May 29, 2009, from http://chronicle.com/daily/2009/05/18511n.htm.
  47. Keller, J. (2009b, June 17). Fees could rise by 30% at California community colleges. Chronicle of Higher Education. Retrieved June 17, 2009, from http://chronicle.com/news/article/6650/fees-could-rise-by-30-at-california-community-colleges.
  48. Kuh, G. D., Hu, S., & Vesper, N. (2000). ‘They shall be known by what they do’: An activities-based typology of college students. Journal of College Student Development, 41, 228–244.Google Scholar
  49. Layzell, D. T. (1999). Linking performance to funding outcomes at the state level for public institutions of higher education: Past, present, and future. Research in Higher Education, 40, 233–246.CrossRefGoogle Scholar
  50. Luan, J., Zhao, C., & Hayek, J. (2004). Exploring a new frontier in higher education research: A case study analysis of using data mining techniques to create NSSE institutional typology. Paper presented at the annual meeting of the California Association for Institutional Research, Anaheim, California.Google Scholar
  51. Manski, C. F. (1989). Schooling as experimentation: A reappraisal of the postsecondary dropout phenomenon. Economics of Education Review, 8, 305–312.CrossRefGoogle Scholar
  52. Margrain, S. A. (1978). Student characteristics and academic performance in higher education: A review. Research in Higher Education, 8, 111–123.CrossRefGoogle Scholar
  53. Mauss, A. L. (1967). Toward an empirical typology of junior college student subcultures. Paper presented at the annual meeting of the Pacific Sociological Association, Long Beach, CA. ERIC Document Reproduction Service No. ED013076.Google Scholar
  54. New budget submitted by Oregon Governor leaves state’s community colleges feeling shortchanged. (2009, January 26). Community College Week, 21(11), 8–9.Google Scholar
  55. Perez, R. G. (2009). California budget battle underscores need to support community colleges. Community College Week, 21(16), 4.Google Scholar
  56. Peseau, B. A., & Tudor, R. L. (1988). Exploring and testing cluster analysis. Research in Higher Education, 29, 60–78.CrossRefGoogle Scholar
  57. Planty, M., Hussar, W., Snyder, T., Kena, G., KewalRamani, A., Kemp, J., et al. (2009). The condition of education 2009 (NCES 2009-081). Washington, DC: National Center for Education Statistics.Google Scholar
  58. Rapkin, B. D., & Luke, D. A. (1993). Cluster analysis in community research: Epistemology and practice. American Journal of Community Psychology, 21, 247–277.CrossRefGoogle Scholar
  59. Ritzer, G. (1996). Classical sociological theory. New York: McGraw-Hill.Google Scholar
  60. Rosenbaum, J. E. (2001). Beyond college for all: Career paths for the forgotten half. New York: Sage.Google Scholar
  61. Santibanez, L., Gonzalez, G., Morrison, P. A., & Carroll, S. J. (2007). Methods for gauging the target populations that community college serve. Population Research and Policy Review, 26, 51–67.CrossRefGoogle Scholar
  62. Schoenecker, C., & Reeves, R. (2008). The National Student Clearinghouse: The largest current student tracking database. New Directions for Community Colleges, 143, 47–57.CrossRefGoogle Scholar
  63. Schuck, P. H., & Zeckhauser, R. J. (2006). Targeting in social programs: Avoiding bad bets, removing bad apples. Washington, DC: Brookings Institution.Google Scholar
  64. Sengupta, R., & Jepsen, C. (2006). California’s community college students. California Counts: Population Trends and Profiles, 8, 1–23.Google Scholar
  65. Shaw, K. M., & Jacobs, J. A. (2003). Community colleges: New environments, new directions. Annals of the American Academic of Political and Social Science, 586, 6–15.CrossRefGoogle Scholar
  66. Shulock, N., & Moore, C. (2007). Rules of the game: How state policy creates barriers to degree completion and impedes student success in the California community colleges. Sacramento, CA: Institute for Higher Education Leadership and Policy.Google Scholar
  67. Skinner, H. A. (1981). Toward the integration of classification theory and methods. Journal of Abnormal Psychology, 90, 68–87.CrossRefGoogle Scholar
  68. StataCorp. (2007). Stata multivariate statistics reference manual, release 10. College Station, TX: StataCorp LP.Google Scholar
  69. Stets, J. E., & Burke, P. J. (2000). Identity theory and social identity theory. Social Psychology Quarterly, 63, 224–237.CrossRefGoogle Scholar
  70. Townsend, B. K. (2002). Transfer rates: A problematic criterion for measuring the community college. New Directions for Community Colleges, 117, 13–23.CrossRefGoogle Scholar
  71. VanDerLinden, K. (2002). Credit student analysis: 1999 and 2000. Annapolis Junction, MD: Community College Press, American Association of Community Colleges.Google Scholar
  72. van Ommeren, A., & Fong-Batkin, L. (2006, November 2). An evaluation of unreported SSNs and implications for policy. Paper presented at the annual meeting of the California Association for Institutional Research, Pasadena, California.Google Scholar
  73. Voorhees, R. A., & Zhou, D. (2000). Intentions and goals at the community college: Associating student perceptions and demographics. Community College Journal of Research and Practice, 24, 219–232.CrossRefGoogle Scholar
  74. Wash. Gov. proposes tuition increases to offset budget cuts. (2009, May 4). Community College Week, 21(18), 9.Google Scholar
  75. 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, 651–672.CrossRefGoogle Scholar
  76. Xu, J. (2008). Using the IPEDS peer analysis system in peer group selection. AIR Professional File, 110, 1–13.Google Scholar
  77. Ziberna, A., & Zabkar, V. (2003). Application of end-users market segmentation using statistical methods. In A. Ferligoj & A. Mrvar (Eds.), Developments in applied statistics (pp. 243–263). Ljubljana, Slovenia: University of Ljubljana.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Center for the Study of Higher and Postsecondary EducationUniversity of MichiganAnn ArborUSA

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