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Major differences: modeling profiles of community college persisters in career clusters

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  • Grant Morgan
  • Mark D’Amico
  • Kari Hodge

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. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Grant Morgan & Mark D’Amico & Kari Hodge, 2015. "Major differences: modeling profiles of community college persisters in career clusters," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 1-20, January.
    • Handle: RePEc:spr:qualqt:v:49:y:2015:i:1:p:1-20
    DOI: 10.1007/s11135-013-9970-x
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    References listed on IDEAS

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    1. Alfonso, Mariana & Bailey, Thomas R. & Scott, Marc, 2005. "The educational outcomes of occupational sub-baccalaureate students: evidence from the 1990s," Economics of Education Review, Elsevier, vol. 24(2), pages 197-212, April.
    2. William Meredith, 1993. "Measurement invariance, factor analysis and factorial invariance," Psychometrika, Springer;The Psychometric Society, vol. 58(4), pages 525-543, December.
    3. Yang, Chih-Chien, 2006. "Evaluating latent class analysis models in qualitative phenotype identification," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1090-1104, February.
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