Maximum Likelihood Analysis of Nonlinear Structural Equation Models with Dichotomous Variables

Autor/inn/en	Song, Xin-Yuan; Lee, Sik-Yum
Titel	Maximum Likelihood Analysis of Nonlinear Structural Equation Models with Dichotomous Variables
Quelle	In: Multivariate Behavioral Research, 40 (2005) 2, S.151-177 (27 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0027-3171
DOI	10.1207/s15327906mbr4002_1
Schlagwörter	Structural Equation Models; Simulation; Computation; Error of Measurement; Evaluation Methods; Measurement Techniques; Statistical Analysis; Maximum Likelihood Statistics; Behavioral Science Research + Suchen Sie Ihr Suchwort? Simulation program; Simulationsprogramm; Messfehler; Messtechnik; Statistische Analyse
Abstract	In this article, a maximum likelihood approach is developed to analyze structural equation models with dichotomous variables that are common in behavioral, psychological and social research. To assess nonlinear causal effects among the latent variables, the structural equation in the model is defined by a nonlinear function. The basic idea of the development is to augment the observed dichotomous data with the hypothetical missing data that involve the latent underlying continuous measurements and the latent variables in the model. An EM algorithm is implemented. The conditional expectation in the E-step is approximated via observations simulated from the appropriate conditional distributions by a Metropolis-Hastings algorithm within the Gibbs sampler, whilst the M-step is completed by conditional maximization. Convergence is monitored by bridge sampling. Standard errors are also obtained. Results from a simulation study and a real example are presented to illustrate the methodology. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10