Measurement and Structural Model Class Separation in Mixture CFA: ML/EM versus MCMC

Autor/in	Depaoli, Sarah
Titel	Measurement and Structural Model Class Separation in Mixture CFA: ML/EM versus MCMC
Quelle	In: Structural Equation Modeling: A Multidisciplinary Journal, 19 (2012) 2, S.178-203 (26 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1070-5511
DOI	10.1080/10705511.2012.659614
Schlagwörter	Markov Processes; Factor Analysis; Statistical Bias; Evaluation Research; Structural Equation Models; Monte Carlo Methods; Error of Measurement; Maximum Likelihood Statistics; Sample Size; Bayesian Statistics + Suchen Sie Ihr Suchwort? Markowscher Prozess; Faktorenanalyse; Evaluationsforschung; Monte-Carlo-Methode; Messfehler
Abstract	Parameter recovery was assessed within mixture confirmatory factor analysis across multiple estimator conditions under different simulated levels of mixture class separation. Mixture class separation was defined in the measurement model (through factor loadings) and the structural model (through factor variances). Maximum likelihood (ML) via the EM algorithm was compared to a Markov chain Monte Carlo (MCMC) estimator condition using weak priors and a condition using tight priors. Results indicated that the MCMC weak condition produced the highest bias, particularly with a weak Dirichlet prior for the mixture class proportions. Specifically, the weak Dirichlet prior affected parameter estimates under all mixture class separation conditions, even with moderate and large sample sizes. With little knowledge about parameters, ML/EM should be used over MCMC weak. However, MCMC tight produced the lowest bias under all mixture class separation conditions and should be used if tight and accurate priors can be placed on parameters. (Contains 7 tables, 2 figures, and 10 footnotes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10