Skip to main content
SpringerLink
Log in

An Empirical Typology of the Latent Programmatic Structure of Community College Student Success Programs

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

Abstract

The definition and description of student success programs in the literature (e.g., orientation, first-year seminars, learning communities, etc.) suggest underlying programmatic similarities. Yet researchers to date typically depend on ambiguous labels to delimit studies, resulting in loosely related but separate research lines and few generalizable findings. To demonstrate whether or how certain programs are effective there is need for more coherent conceptualizations to identify and describe programs. This is particularly problematic for community colleges where success programs are uniquely tailored relative to other sectors. The study’s purpose is to derive an empirical typology of community college student success programs based on their curricular and programmatic features. Data come from 1047 success programs at 336 U.S.-based respondents to the Community College Institutional Survey. Because programs might be characterized by their focus in different curricular areas and combinations of foci, we used factor mixture modeling, a hybrid of factor analysis and latent class analysis, which provides a model-based classification method that simultaneously accounts for dimensional and categorical data structures. Descriptive findings revealed extensive commonalities among nominal program types. Inferential analysis revealed five factors (types) of program elements, combined in unique ways among four latent program types: success skills programs, comprehensive programs, collaborative academic programs, and minimalist programs. We illustrate how the typology deconstructs nominal categories, may help unify different bodies of research, and affords a common framework and language for researchers and practitioners to identify and conceptualize programs based on what they do rather than by their names.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Astin, A. W., & Antonio, A. L. (2012). Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education (2nd ed.). Lanham, MD: Rowman & Littlefield.

    Google Scholar 

  • Ayres, L., & Knafl, K. (2008). Typological analysis. In Lisa M. Given (Ed.), The Sage encyclopedia of qualitative research methods (pp. 901–903). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Bahr, P. R. (2010). The bird’s eye view of community colleges: A behavioral typology of first-time students based on cluster analytic classification. Research in Higher Education, 51(8), 724–749.

    Article  Google Scholar 

  • Bailey, T., & Alfonso, M. (2005). Paths to persistence: An analysis of research on program effectiveness at community colleges. Indianapolis, IN: Lumina Foundation for Education.

    Google Scholar 

  • Barefoot, B. O. (1992). Helping first-year college students climb the academic ladder: Report of a national survey of freshman seminar programming in American higher education (Doctoral dissertation). Retrieved from ProQuest Dissertation and Theses Database (AAT No. 9226630).

  • Beatty-Guenter, P. (1994). Sorting, supporting, connecting, and transforming: Retention strategies at community colleges. Community College Journal of Research and Practice, 18(2), 113–129.

    Article  Google Scholar 

  • Bonett, D. G., & Price, R. M. (2005). Inferential methods for the tetrachoric correlation coefficient. Journal of Educational and Behavioral Statistics, 30(2), 213–225.

    Article  Google Scholar 

  • Brock, T., Jenkins, D., Ellwein, T., Miller, J., Gooden, S., Martin, K., et al. (2007). Building a culture of evidence for community college student success: Early progress in the Achieving the Dream Initiative. New York: MDRC.

    Google Scholar 

  • Brownell, J. E., & Swaner, L. E. (2009). High-impact practices: Applying the learning outcomes literature to the development of successful campus programs. Peer Review, 11(2), 26–30.

    Google Scholar 

  • Cagnone, S., & Viroli, C. (2010). A factor mixture analysis model for multivariate binary data. arXiv:1010.2314. Retrieved from http://arxiv.org/abs/1010.2314.

  • Center for Community College Student Engagement. (2012). A matter of degrees: Promising practices for community college student success (a first look). Austin, TX: The University of Texas at Austin, Community College Leadership Program.

    Google Scholar 

  • Clark, S. L., Muthén, B. O., Kaprio, J., D’Onofrio, B. M., Viken, R., & Rose, R. J. (2013). Models and strategies for factor mixture analysis: An example concerning the structure underlying psychological disorders. Structural Equation Modeling, 20(4), 681–703.

    Article  Google Scholar 

  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9.

    Google Scholar 

  • Crisp, G., & Taggart, A. (2013). Community college student success programs: A synthesis, critique, and research agenda. Community College Journal of Research and Practice, 37(2), 114–130.

    Article  Google Scholar 

  • Denson, N., & Ing, M. (2014). Latent class analysis in higher education: An illustrative example of pluralistic orientation. Research in Higher Education, 55(5), 1–19.

    Article  Google Scholar 

  • Duggan, M. H., & Williams, M. R. (2010). Community college student success courses: The student perspective. Community College Journal of Research and Practice, 35(1–2), 121–134.

    Article  Google Scholar 

  • Edgecombe, N. (2011). Accelerating the academic achievement of students referred to developmental education (CCRC Working Paper No. 30). New York, NY: Community College Research Center, Teachers College, Columbia University.

  • Edgecombe, N., Jaggars, S. S., Baker, E. D., & Bailey, T. (2013). Acceleration through a holistic support model: An implementation and outcomes analysis of FastStart@CCD. New York: Community College Research Center, Teachers College, Columbia University.

    Google Scholar 

  • Fidler, P. (1991). Relationship of freshman orientation seminars to sophomore return rates. Journal of the First-Year Experience & Students in Transition, 3(1), 7–38.

    Google Scholar 

  • Fong, K., & Visher, M. G. (2013). Fast forward: A case study of two community college programs designed to accelerate students through developmental math. New York: MDRC.

    Google Scholar 

  • Friedman, D., & Alexander, J. (2007). Investigating a first-year seminar as an anchor course in learning communities. Journal of the First-Year Experience & Students in Transition, 19(1), 63–74.

    Google Scholar 

  • Gabelnick, F., MacGregor, J., Matthews, R., & Smith, B. L. (1990). Learning community models. New Directions for Teaching and Learning, 1990(41), 19–37.

    Article  Google Scholar 

  • Gardner, J. N. (1986). The freshman year experience. College and University, 61(4), 261–274.

    Google Scholar 

  • Goodman, K., & Pascarella, E. T. (2006). First-year seminars increase persistence and retention: A summary of evidence from How College Affects Students. Peer Review, 8(3), 26–28.

    Google Scholar 

  • Hatch, D. K., & Bohlig, E. M. (2015). The scope and design of structured group learning experiences at community colleges. Community College Journal of Research and Practice, 39(9), 819–838.

    Article  Google Scholar 

  • Henscheid, J. M. (Ed.). (2004). Integrating the first-year experience: The role of first-year seminars in learning communities. Columbia, SC: National Resource Center for the First-Year Experience and Students in Transition, University of South Carolina.

  • Henson, J. M., Reise, S. P., & Kim, K. H. (2007). Detecting mixtures from structural model differences using latent variable mixture modeling: A comparison of relative model fit statistics. Structural Equation Modeling, 14(2), 202–226.

    Article  Google Scholar 

  • Hodara, M., & Jaggars, S. S. (2014). An examination of the impact of accelerating community college students’ progression through developmental education. The Journal of Higher Education, 85(2), 246–276.

    Article  Google Scholar 

  • Hurtado, S., Astin, A. W., & Dey, E. L. (1991). Varieties of general education programs: An empirically based taxonomy. The Journal of General Education, 40, 133–162.

    Google Scholar 

  • Inkelas, K. K., Soldner, M., Longerbeam, S. D., & Brown Leonard, J. (2008). Differences in student outcomes by types of living–learning programs: The development of an empirical typology. Research in Higher Education, 49(6), 495–512.

    Article  Google Scholar 

  • Jenkins, D., Speroni, C., Belfield, C., Jaggars, S. S., & Edgecombe, N. (2010). A model for accelerating academic success of community college remedial English students: Is the Accelerated Learning Program (ALP) effective and affordable?. New York: Community College Research Center, Teachers College, Columbia University.

    Google Scholar 

  • Karp, M. M. (2011). Toward a new understanding of non-academic student support: Four mechanisms encouraging positive student outcomes in the community college (CCRC Working Paper No. 28). New York: Community College Research Center, Teachers College, Columbia University.

  • Keup, J. R., & Barefoot, B. O. (2005). Learning how to be a successful student: Exploring the impact of first-year seminars on student outcomes. Journal of the First-Year Experience & Students in Transition, 17(1), 11–47.

    Google Scholar 

  • Kim, S. H. (2012). Max Weber. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Fall 2012). Retrieved from http://plato.stanford.edu/archives/fall2012/entries/weber/.

  • Koski, W. S., & Levin, H. M. (1998). Replacing remediation with acceleration in higher education: Preliminary report on literature review and initial interviews. Stanford, CA: National Center for Postsecondary Improvement, Stanford University. http://www.stanford.edu/group/ncpi/documents/pdfs/4-01_remediation.pdf. Accessed 29 February 2012.

  • Kubinger, K. D. (2003). On artificial results due to using factor analysis for dichotomous variables. Psychology Science, 45(1), 106–110.

    Google Scholar 

  • Kuh, G. D. (2008). High-impact educational practices: What they are, who has access to them, and why they matter. Washington, DC: Association of American Colleges and Universities.

    Google Scholar 

  • Kuh, G. D., & O’Donnell, K. (2013). Ensuring quality & taking high-impact practices to scale. Washington, DC: Association of American Colleges and Universities.

    Google Scholar 

  • Kulik, C.-L. C., Kulik, J. A., & Shwalb, B. J. (1983). College programs for high-risk and disadvantaged students: A meta-analysis of findings. Review of Educational Research, 53(3), 397–414.

    Article  Google Scholar 

  • Lenning, O. T., & Ebbers, L. H. (1999). The powerful potential of learning communities: Improving education for the future. Washington, DC: George Washington University, Graduate School of Education and Human Development.

    Google Scholar 

  • Lester, J. (2014). The completion agenda: The unintended consequences for equity in community colleges. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (Vol. 29, pp. 423–466). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Levin, J. S., Cox, E. M., Cerven, C., & Haberler, Z. (2010). The recipe for promising practices in community colleges. Community College Review, 38(1), 31–58.

    Article  Google Scholar 

  • Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778.

    Article  Google Scholar 

  • Love, A. G., & Tokuno, K. A. (1999). Learning community models. In J. H. Levine (Ed.), Learning communities: New structures, new partnerships for learning (pp. 9–18). Columbia, SC: National Resource Center for the First-Year Experience and Students in Transition, University of South Carolina.

    Google Scholar 

  • Lubke, G., & Muthén, B. O. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10(1), 21–39.

    Article  Google Scholar 

  • Malnarich, G. (2005). Learning communities and curricular reform: “Academic apprenticeships” for developmental students. New Directions for Community Colleges, 2005(129), 51–62.

    Article  Google Scholar 

  • Mangan, K. (2015, February 26). Program’s extra support for community-college students is paying off. The Chronicle of Higher Education. Retrieved from http://chronicle.com/article/Programs-Extra-Support-for/190415/.

  • Marti, C. N. (2008). Latent postsecondary persistence pathways: Educational pathways in American two-year colleges. Research in Higher Education, 49(4), 317–336.

    Article  Google Scholar 

  • Masyn, K. E., Henderson, C. E., & Greenbaum, P. E. (2010). Exploring the latent structures of psychological constructs in social development using the dimensional–categorical spectrum. Social Development, 19(3), 470–493.

    Article  Google Scholar 

  • Mills, M. T. (2010). Tools of engagement: Success course influence on student engagement. Journal of the First-Year Experience & Students in Transition, 22(2), 9–31.

    Google Scholar 

  • Minkler, J. E. (2002). ERIC review: Learning communities at the community college. Community College Review, 30(3), 46–63.

    Article  Google Scholar 

  • Morin, A. J. S., Morizot, J., Boudrias, J.-S., & Madore, I. (2011). A multifoci person-centered perspective on workplace affective commitment: A latent profile/factor mixture analysis. Organizational Research Methods, 14(1), 58–90.

    Article  Google Scholar 

  • Muthén, B. O. (1989). Dichotomous factor analysis of symptom data. Sociological Methods & Research, 18(1), 19–65.

    Article  Google Scholar 

  • Muthén, B. O., & Muthén, L. K. (2010). Mplus (version 6). Los Angeles: Muthén & Muthén.

    Google Scholar 

  • Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569.

    Article  Google Scholar 

  • O’Gara, L., Karp, M. M., & Hughes, K. L. (2009). Student success courses in the community college. Community College Review, 36(3), 195–218.

    Article  Google Scholar 

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

    Google Scholar 

  • Pascarella, E. T., Terenzini, P. T., & Wolfle, L. M. (1986). Orientation to college and freshman year persistence/withdrawal decisions. The Journal of Higher Education, 57(2), 155–175.

    Article  Google Scholar 

  • Perrine, R. M., & Spain, J. W. (2008). Impact of a pre-semester college orientation program: Hidden benefits? Journal of College Student Retention: Research, Theory and Practice, 10(2), 155–169.

    Article  Google Scholar 

  • Pike, G. R., Hansen, M. J., & Lin, C.-H. (2011). Using instrumental variables to account for selection effects in research on first-year programs. Research in Higher Education, 52(2), 194–214.

    Article  Google Scholar 

  • Porter, S. R., & Swing, R. L. (2006). Understanding how first-year seminars affect persistence. Research in Higher Education, 47(1), 89–109.

    Article  Google Scholar 

  • Price, D. V., & Tovar, E. (2014). Student engagement and institutional graduation rates: Identifying high-impact educational practices for community colleges. Community College Journal of Research and Practice, 38(9), 766–782.

    Article  Google Scholar 

  • Rice, R. (1992). Reactions of participants to either one-week pre-college orientation or to freshman seminar courses. Journal of the First-Year Experience & Students in Transition, 4(2), 85–100.

    Google Scholar 

  • Robinson, D. A. G., Burns, C. F., & Gaw, K. F. (1996). Orientation programs: A foundation for student learning and success. New Directions for Student Services, 1996(75), 55–68.

    Article  Google Scholar 

  • SAS Institute, Inc. (2012). Sample 25010: Create a polychoric correlation or distance matrix. http://support.sas.com/kb/25/010.html. Accessed 21 March 2012.

  • Schnell, C. A., & Doetkott, C. D. (2003). First year seminars produce long-term impact. Journal of College Student Retention: Research, Theory and Practice, 7(4), 377–391.

    Article  Google Scholar 

  • Schnell, C. A., Louis, K., & Doetkott, C. (2003). The first-year seminar as a means of improving college graduation rates. Journal of the First-Year Experience & Students in Transition, 15(1), 53–76.

    Google Scholar 

  • Scrivener, S., Weiss, M. J., Ratledge, A., Rudd, T., Sommo, C., & Fresques, H. (2015). Doubling graduation rates: Three-year effects of CUNY’s accelerated study in associate programs (ASAP) for developmental education students. Washington, DC: MDRC.

    Google Scholar 

  • Shapiro, N. S., & Levine, J. H. (1999). Creating learning communities: A practical guide to winning support, organizing for change, and implementing programs. San Francisco: Jossey-Bass.

    Google Scholar 

  • Smith, R. A. (2010). Feeling supported: Curricular learning communities for basic skills courses and students who speak English as a second language. Community College Review, 37(3), 261–284.

    Article  Google Scholar 

  • Smith, B. L., & Hunter, M. R. (1988). Learning communities: A paradigm for educational revitalization. Community College Review, 15(4), 45–51.

    Article  Google Scholar 

  • Strayhorn, T. (2009). An examination of the impact of first-year seminars on correlates of college student retention. Journal of the First-Year Experience & Students in Transition, 21(1), 9–27.

    Google Scholar 

  • Strumpf, G., & Hunt, P. (1993). The effects of an orientation course on the retention and academic standing of entering freshmen, controlling for the volunteer effect. Journal of the Freshman Year Experience, 5(1), 7–14.

    Google Scholar 

  • Swaner, L. E., & Brownell, J. E. (2009). Outcomes of high impact practices for underserved students: A review of the literature. Prepared for the Association of American Colleges and Universities (AAC&U) Project USA. http://www.aacu.org/inclusive_excellence/documents/DRAFTProjectUSALiteratureReview.pdf.

  • Tinto, V., & Russo, P. (1994). Coordinated studies programs: Their effect on student involvement at a community college. Community College Review, 22(2), 16–25.

    Article  Google Scholar 

  • Tobolowsky, B. F., Cox, B. E., & Wagner, M. T. (2005). Exploring the evidence: Reporting research on first-year seminars, Volume III (Monograph No. 42). Columbia, SC: National Resource Center for the First-Year Experience and Students in Transition, University of South Carolina. Retrieved from http://www.sc.edu/fye/resources/fyr/index.html.

  • Trowler, P. (2012). Wicked issues in situating theory in close-up research. Higher Education Research & Development, 31(3), 273–284.

    Article  Google Scholar 

  • Weber, M. (1949). The methodology of the social sciences. New York: Free Press.

    Google Scholar 

  • Weerts, D. J., Cabrera, A. F., & Pérez Mejías, P. (2014). Uncovering categories of civically engaged college students: A latent class analysis. The Review of Higher Education, 37(2), 141–168.

    Article  Google Scholar 

  • Weiss, M. J., Bloom, H. S., & Brock, T. (2013). A conceptual framework for studying the sources of variation in program effects. (MDRC Working Paper). New York, NY: MDRC. Retrieved from http://www.mdrc.org/publication/conceptual-framework-studying-sources-variation-program-effects.

  • Whyte, C. B. (2007). An additional look at orientation programs nationally. The Journal of College Orientation and Transition, 15(1), 71–77.

    Google Scholar 

  • Young, D. G., & Hopp, J. M. (2014). 2012–2013 National Survey of First-Year Seminars: Exploring high-impact practices in the first college year. Columbia, SC: National Resource Center for the First-Year Experience and Students in Transition, University of South Carolina.

  • Zachry-Rutschow, E. M., & Schneider, E. (2011). Unlocking the gate: What we know about improving developmental education. Washington, DC: MDRC.

    Google Scholar 

  • Zhao, C.-M., & Kuh, G. D. (2004). Adding value: Learning communities and student engagement. Research in Higher Education, 45(2), 115–138.

    Article  Google Scholar 

  • Zimmerman, A. (2000). A journal-based orientation course as a predictor of student success at a public two-year technical college. Journal of the First-Year Experience & Students in Transition, 12(1), 29–43.

    Google Scholar 

Download references

Acknowledgments

This research was derived from a broader research and practice-improvement initiative focused on identifying and promoting high-impact educational practices in community colleges, conducted by the Center for Community College Student Engagement, through the generous funding of the Bill and Melinda Gates Foundation and the Lumina Foundation. The research findings reflect the opinion of the authors and not necessarily those of the Gates Foundation or Lumina Foundation.

Author information

Authors and Affiliations

  1. University of Nebraska–Lincoln, 119 Teachers College Hall, Lincoln, NE, 68588-0360, USA

    Deryl K. Hatch

  2. Center for Community College Student Engagement, The University of Texas at Austin, 3316 Grandview St., Austin, TX, 78705, USA

    E. Michael Bohlig

Authors
  1. Deryl K. Hatch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Michael Bohlig
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deryl K. Hatch.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hatch, D.K., Bohlig, E.M. An Empirical Typology of the Latent Programmatic Structure of Community College Student Success Programs. Res High Educ 57, 72–98 (2016). https://doi.org/10.1007/s11162-015-9379-6

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11162-015-9379-6

Keywords

Search

Navigation