A Simple Approach to Inference in Covariance Structure Modeling with Missing Data: Bayesian Analysis. Project 2.4, Quantitative Models To Monitor the Status and Progress of Learning and Performance and Their Antecedents.

Autor/in	Muthen, Bengt
Institution	National Center for Research on Evaluation, Standards, and Student Testing, Los Angeles, CA.
Titel	A Simple Approach to Inference in Covariance Structure Modeling with Missing Data: Bayesian Analysis. Project 2.4, Quantitative Models To Monitor the Status and Progress of Learning and Performance and Their Antecedents.
Quelle	(1994), (13 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Bayesian Statistics; Estimation (Mathematics); Maximum Likelihood Statistics; Monte Carlo Methods; Simulation; Statistical Inference + Suchen Sie Ihr Suchwort? Estimation; Mathematics; Schätzung; Monte-Carlo-Methode; Simulation program; Simulationsprogramm; Inferential statistics; Schließende Statistik
Abstract	This paper investigates methods that avoid using multiple groups to represent the missing data patterns in covariance structure modeling, attempting instead to do a single-group analysis where the only action the analyst has to take is to indicate that data is missing. A new covariance structure approach developed by B. Muthen and G. Arminger is used. The approach draws on Bayesian theory and is a full-information estimator as is maximum-likelihood estimation. The proposed methodology is described briefly, and tests of its performance on simulated data in a Monte Carlo study under various forms of missing data are reviewed. This easy-to-use approach results in good properties for the parameter estimates. The technique is not, however, yet available in covariance structure software. Two tables are included. (Contains 6 references.) (SLD)
Erfasst von	ERIC (Education Resources Information Center), Washington, DC