Bayesian Factor Analysis When Only a Sample Covariance Matrix Is Available

Autor/inn/en	Hayashi, Kentaro; Arav, Marina
Titel	Bayesian Factor Analysis When Only a Sample Covariance Matrix Is Available
Quelle	In: Educational and Psychological Measurement, 66 (2006) 2, S.272-284 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164405278583
Schlagwörter	Bayesian Statistics; Factor Analysis; Correlation; Matrices; Computation; Structural Equation Models; Alternative Assessment; Shift Studies; Methods Research; Maximum Likelihood Statistics + Suchen Sie Ihr Suchwort? Faktorenanalyse; Korrelation; Matrizenrechnung; Schichtunterricht; Methodenforschung
Abstract	In traditional factor analysis, the variance-covariance matrix or the correlation matrix has often been a form of inputting data. In contrast, in Bayesian factor analysis, the entire data set is typically required to compute the posterior estimates, such as Bayes factor loadings and Bayes unique variances. We propose a simple method for computing the posterior estimates of Bayesian factor analysis using only the sample variance-covariance matrix without the entire data set. The method is verified in terms of an existing data set. With our method, researchers will be able to apply Bayesian factor analysis when they find either a variance-covariance or a correlation matrix with standard deviations in the existing literature. (Contains 2 tables.) (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10