Mixed-Effects Logistic Regression Models for Indirectly Observed Discrete Outcome Variables

Autor/in	Vermunt, Jeroen K.
Titel	Mixed-Effects Logistic Regression Models for Indirectly Observed Discrete Outcome Variables
Quelle	In: Multivariate Behavioral Research, 40 (2005) 3, S.281-301 (21 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0027-3171
DOI	10.1207/s15327906mbr4003_1
Schlagwörter	Predictor Variables; Correlation; Maximum Likelihood Statistics; Error of Measurement; Computation; Mathematical Models; Observational Learning; Regression (Statistics); Industrial Psychology; Methods Research + Suchen Sie Ihr Suchwort? Prädiktor; Korrelation; Messfehler; Mathematical model; Mathematisches Modell; Imitationslernen; Regression; Regressionsanalyse; Betriebspsychologie; Industriepsychologie; Methodenforschung
Abstract	A well-established approach to modeling clustered data introduces random effects in the model of interest. Mixed-effects logistic regression models can be used to predict discrete outcome variables when observations are correlated. An extension of the mixed-effects logistic regression model is presented in which the dependent variable is a latent class variable. This method makes it possible to deal simultaneously with the problems of correlated observations and measurement error in the dependent variable. As is shown, maximum likelihood estimation is feasible by means of an EM algorithm with an E step that makes use of the special structure of the likelihood function. The new model is illustrated with an example from organizational psychology. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10