STEMM: A General Finite Mixture Structural Equation Model

Journal of Classification, Volume 14, Issue 1, pp 23-50, 1997

28 Pages Posted: 9 Jun 2016

See all articles by Kamel Jedidi

Kamel Jedidi

Columbia Business School - Marketing

Harsharanj S. Jagpal

Rutgers, The State University of New Jersey

Wayne S. DeSarbo

Pennsylvania State University

Date Written: January 1, 1997

Abstract

This paper provides a general Structural Equation finite Mixture Model and algorithm (STEMM). Substantively, the model allows the researcher to simul­taneously treat heterogeneity and form groups in the context of a postulated causal (i.e., simultaneous equation regression) structure in which all the observables are measured with error. Methodologically, the model is more general than such sta­tistical methods as cluster analysis, confirmatory multigroup factor analysis, and multigroup structural equation models. In particular the general finite mixture model includes, as special cases, finite mixtures of simultaneous equations with feedback, confirmatory factor analysis, and confirmatory second-order factor models. We describe the statistical theory, present simulation evidence on the per­formance of the EM estimation algorithm, and apply the model to a psychological study on the role of emotion in goal-directed behavior. Finally we discuss several avenues for future research.

Keywords: Structural equations, Finite mixtures, Maximum likelihood, Emo­tions, Weight loss

Suggested Citation

Jedidi, Kamel and Jagpal, Harsharanj S. and DeSarbo, Wayne S., STEMM: A General Finite Mixture Structural Equation Model (January 1, 1997). Journal of Classification, Volume 14, Issue 1, pp 23-50, 1997. Available at SSRN: https://ssrn.com/abstract=2791766

Kamel Jedidi

Columbia Business School - Marketing ( email )

New York, NY 10027
United States

Harsharanj S. Jagpal

Rutgers, The State University of New Jersey ( email )

311 North 5th Street
New Brunswick, NJ 08854
United States

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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