Abstract
The purpose is to explore factors associated with community college student persistence in academic program areas through the modeling of student profiles (i.e., classes) using selected variables from the Education Longitudinal Study of 2002 (ELS:2002) conducted by the National Center for Education Statistics that has followed a national sample of students from the tenth grade with follow-ups 2 and 4 years later. To this end, we used multiple-group latent class analysis in order to identify underlying classes of students and to evaluate the equivalence of the latent class solution across those students who persisted and those who did not. A four-class solution was identified that was determined to be invariant across student groups although the proportions of persisters and nonpersisters were different across classes. Using the final class solution for persisting students, we found that class membership was moderately associated with which Career Cluster students pursued.
Similar content being viewed by others
References
Alfonso, M., Bailey, T.R., Scott, M.: The educational outcomes of occupational sub-baccalaureate students: evidence from the 1990s. Econ. Educ. Rev. 24, 197–212 (2005)
American Association of Community Colleges (AACC). Community college fact sheet. Author, Washington, DC. http://www.aacc.nche.edu/AboutCC/Pages/fastfactsfactsheet.aspx (2013)
Bailey, T., Kienzl, G., Marcotte, D.: Who benefits from postsecondary occupational education? Findings from the 1980s and 1990s (CCRC Brief No. 23). Community College Research Center, Teachers College, Columbia University, New York (2004)
Banfield, J.D., Raferty, A.E.: Model-based Gaussian and non-Gaussian clustering. Biometrics 49, 803–821 (1993)
Carnevale, A. P., Cheah, B.: Hard times: college majors, unemployment and earnings. Center on Education and the Workforce, Georgetown University, Washington, DC. http://www9.georgetown.edu/grad/gppi/hpi/cew/pdfs/HardTimes.2013.2.pdf (2013)
Carnevale, A.P., Smith, N., Stone, J.R., III., Kotamraju, P., Steuernagel, B., Green, K.A.: Career clusters: forecasting demand for high school through college jobs 2008–2018. Center on Education and the Workforce, Georgetown University, Washington, DC. http://www9.georgetown.edu/grad/gppi/hpi/cew/pdfs/clusters-complete-update1.pdf (2011)
Carnevale, A.P., Smith, N., Strohl, J.: Help wanted: projections of jobs and education requirements through 2018. Center on Education and the Workforce, Georgetown University, Washington, DC. www.cew.georgetown.edu/JOBS2018/ (2010)
Chen, X.: Students who study science, technology, engineering, and mathematics (STEM) in postsecondary education: stats in brief (NCES 2009-161). National Center for Education Statistics, Washington, DC (2009)
Clery, S.: Characteristics of credential completers. Data Notes: Keeping Informed about Achieving the Dream Data 6(2) (2011)
Collins, L.M., Lanza, S.T.: Latent class and latent transition analysis: with applications in the social, behavioral, and health sciences. Wiley, Hoboken (2010)
Compton, J.I., Laanan, F.S., Starobin, S.S.: Career and technical education as pathways: factors influencing postcollege earnings of selected career clusters. J. Educ. Stud. Placed Risk 15, 93–113 (2010)
D’Amico, M.M., Morgan, G.B., Robertson, T.C.: Student achievement in identified workforce clusters: understanding factors that influence student success. Community Coll. J. Res. Pract. 35, 773–790 (2011)
Dadgar, M., Weiss, M.J.: Labor market returns to sub-baccalaureate credentials: how much does a community college degree or certificate pay?. (CCRC Working Paper No. 45). Community College Research Center, Teachers College, Columbia University, New York (2012)
Everitt, B.S.: Cluster analysis, 3rd edn. Wiley, New York (1993)
Gantt, A.J.: Graduation rates of students in technical programs at an urban community college. Community Coll. J. Res. Pract. 34, 227–239 (2010)
Grubb, W.N.: The returns to education in the sub-baccalaureate labor market, 1984–1990. Econ. Educ. Rev. 16, 231–245 (1997)
Henson, J.M., Reise, S.P., Kim, K.H.: Detecting mixtures from structural model differences using latent variable mixture modeling: a comparison of relative model fit statistics. Struct. Equ. Model. 14(2), 202–226 (2007)
Hirschy, A.S., Bremer, C.D., Castellano, M.: Career and technical education (CTE) student success in community colleges: a conceptual model. Community Coll. Rev. 39(3), 296–318 (2011)
Illich, P.A., Hagan, C., McCallister, L.: Performance in college-level courses among those concurrently enrolled in remedial courses: policy implications. Community Coll. J. Res. Pract. 28, 435–453 (2004)
Jenkins, D., Cho, S.-W.: Get with the program: accelerating community college students’ entry into and completion of programs of study (CCRC Working Paper No. 32). Community College Research Center, Teachers College, Columbia University, New York (2012)
Kim, D., Saatcioglu, A., Neufeld, A.: College departure: exploring student aid effects on multiple mobility patterns from four-year institutions. J. Stud. Financ. Aid 42(3), 3–24 (2012)
Kolajo, E.F.: From developmental education to graduation: a community college experience. Community Coll. J. Res. Pract. 28, 365–371 (2004)
Lee, J.B.: Different paths for different majors. Data Notes: Keeping Informed about Achieving the Dream Data, 2 (1) (2007)
Lee, J., Judy, J.: Choosing a STEM path: course-sequencing in high school and postsecondary outcomes (ED528924). Society for Research on Educational Effectiveness, Evanston (2011)
Lohman, E.M., Dingerson, M.R.: The effectiveness of occupational-technical certificate programs: assessing student career goals. Community Coll. J. Res. Pract. 29, 339–355 (2005)
Maguire, K.J., Starobin, S.S., Laanan, F.S., Friedel, J.N.: Measuring the accountability of CTE programs: factors that influence postcollege earnings among community college students. Career Tech. Educ. Res. 37, 235–261 (2012)
McLachlan, G.J., Basford, K.E.: Mixture models: inference and applications to clustering. Marcel Dekker, New York (1988)
Meredith, W.: Measurement invariance, factor analysis, and factorial invariance. Psychometrika 58, 525–543 (1993)
Moore, C., Shulock, N.: Sense of direction: the importance of helping community college students select and enter a program of study. California State University Sacramento Institute for Higher Education Leadership & Policy, Sacramento (2011)
Morgan, G.B.: Mixed mode latent class analysis: an examination of fit index performance for classification. Struct. Equ. Model. (in press)
Muthén, L.K., Muthén, B.O.: Statistical analysis with latent variables. Mplus User’s guide, 1998–2012. Muthén & Muthén, Los Angeles (2012)
National Association of State Directors of Career Technical Education Consortium: The 16 Career Clusters. Author, Silver Spring. http://www.careertech.org/career-clusters/ (2013)
National Center for Educational Statistics (NCES): Classification of instructional programs (CIP): CIP user cite. Author, Washington, DC. http://nces.ed.gov/ipeds/cipcode/ (n.d.)
Nitecki, E.M.: The power of the program: how the academic program can improve community college student success. Community Coll. Rev. 39, 98–120 (2011)
Nylund, K.L., Asparouhov, T., Muthén, B.O.: Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct. Equ. Model. 14, 535–569 (2007)
Perkins Collaborative Resource Network Perkins IV crosswalks. U.S. Department of Education, Office of Vocational and Adult Education, Division of Academic and Technical Education, Washington, DC. http://cte.ed.gov/accountability/crosswalks.cfm (2007)
Raykov, T., Marcoulides, G.A., Millsap, R.E.: Factorial invariance in multiple populations: a multiple testing procedure. Educ. Psychol. Meas. 73(4), 713–727 (2012)
Reise, S.P., Widaman, K.F., Pugh, R.H.: Confirmatory factor analysis and item response theory: two approaches for exploring measurement invariance. Psychol. Bull 114, 552–566 (1993)
Rosenfeld, S.A.: Community college/cluster connections: specialization and competitiveness in the U.S. and Europe. Community College Research Center, Teachers College, Columbia University, New York (1998)
SAS Institute, Inc. SAS 9.2: Help and documentation. SAS Institute Inc., Cary (2008)
Tofighi, D., Enders, C.K.: Identifying the correct number of classes in growth mixture models. In: Hancock, G.R., Samuelson, K.M. (eds.) Advances in latent variable mixture models, pp. 317–341. Information Age Publishing Inc., Greenwich (2008)
U.S. Department of Education Education Longitudinal Study of 2002: Base Year Data File User’s Manual. National Center for Education Statistics, Institute of Education Sciences, Washington, DC (2004)
Vandenberg, R.J., Lance, C.E.: A review and synthesis of the measurement invariance literature: suggestions, practices, and recommendations for organizational research. Organ. Res. Methods 3, 4–70 (2000)
Vermunt, J.K., Magidson, J.: Latent class cluster analysis. In: Hagenaars, J.A., McCutcheon, A.L. (eds.) Applied latent class analysis, pp. 89–106. Cambridge University Press, Cambridge (2002)
Widaman, K.F., Reise, S.P.: Exploring the measurement invariance of psychological instruments: applications in the substance use domain. In: Bryant, K.J., Windle, M., West, S.G. (eds.) The science of prevention: methodological advances from alcohol and substance abuse research, pp. 281–324. APA, Washington, DC (1997)
Yang, C.: Evaluating latent class analysis models in qualitative phenotype identification. Comput. Stat. Data Anal. 50, 1090–1104 (2006)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Morgan, G.B., D’Amico, M.M. & Hodge, K.J. Major differences: modeling profiles of community college persisters in career clusters. Qual Quant 49, 1–20 (2015). https://doi.org/10.1007/s11135-013-9970-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11135-013-9970-x