Classical Latent Profile Analysis of Academic Self-Concept Dimensions: Synergy of Person- and Variable-Centered Approaches to Theoretical Models of Self-Concept

Abstract

In this investigation, we used a classic latent profile analysis (LPA), a person-centered approach, to identify groups of students who had similar profiles for multiple dimensions of academic self-concept (ASC) and related these LPA groups to a diverse set of correlates. Consistent with a priori predictions, we identified 5 LPA groups representing a combination of profile level (high vs. low overall ASC) and profile shape (math vs. verbal self-concepts) that complemented results based on a traditional variable-centered approach. Whereas LPA groups were substantially and logically related to the set of 10 correlates, much of the predictive power of individual ASC factors was lost in the formation of groups and the inclusion of the correlates into the LPA distorted the nature of the groups. LPA issues examined include distinctions between quantitative (level) and qualitative (shape) differences in LPA profiles, goodness of fit and the determination of the number of LPA groups, appropriateness of correlates as covariates or auxiliary variables, and alternative approaches to present and interpret the results.

ACKNOWLEDGMENTS

Work on this investigation was conducted, in part, while Professor Herbert W. Marsh was a Visiting Scholar at the Center for Educational Research at the Max Planck Institute for Human Development in Berlin. This research was funded in part by a grant from the United Kingdom Economic and Social Research Council to the first author and support from the Max Planck Institute. The authors would like to thank Bengt Muthén, Gitta Lubke, Dena Pastor, John Uebersax, and Jacqueline Cheng for comments on earlier drafts of this article. Requests for further information about this investigation should be sent to Professor Herbert W. Marsh, Dept. of Education, University of Oxford, 15 Norham Gardens, Oxford OX2 6PY, UK; E-mail: [email protected].

Notes

*p < .01.

*p < .001 (df = 4).