A Comparison of Imputation Methods for Bayesian Factor Analysis Models

Journal of Educational and Behavioral Statistics - Tập 36 Số 2 - Trang 257-276 - 2011
Edgar C. Merkle1
1Wichita State University

Tóm tắt

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.

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Thông tin
Thông tin xuất bản

Journal of Educational and Behavioral Statistics

Tập 36 Số 2

257-276

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