A Comparison of Imputation Methods for Bayesian Factor Analysis Models

Autor/in	Merkle, Edgar C.
Titel	A Comparison of Imputation Methods for Bayesian Factor Analysis Models
Quelle	In: Journal of Educational and Behavioral Statistics, 36 (2011) 2, S.257-276 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998610375833
Schlagwörter	Statistical Analysis; Factor Analysis; Bayesian Statistics; Comparative Analysis; Sample Size; Computation; Statistical Bias + Suchen Sie Ihr Suchwort? Statistische Analyse; Faktorenanalyse
Abstract	Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete data set that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this article compares imputation under an unrestricted multivariate normal model (Multiple Imputation [MI]) to imputation under the statistical model of interest (Data Augmentation [DA]). The former method is popular in applied research, but the latter method is more straightforward from a Bayesian perspective. Simulations demonstrate that DA yields less-biased parameter estimates for moderate sample sizes and high missingness proportions. MI, however, yields less-biased parameter estimates for large sample sizes with misspecified models. The incorporation of auxiliary variables in DA is also addressed, and BUGS code is provided. (Contains 1 figure, 3 tables and 4 notes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10