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A Comparison of Imputation Methods for Bayesian Factor Analysis Models

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  • Edgar C. Merkle

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.

Suggested Citation

  • Edgar C. Merkle, 2011. "A Comparison of Imputation Methods for Bayesian Factor Analysis Models," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 257-276, April.
    • Handle: RePEc:sae:jedbes:v:36:y:2011:i:2:p:257-276
    DOI: 10.3102/1076998610375833
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    References listed on IDEAS

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    1. Jae Kwang Kim, 2004. "Fractional hot deck imputation," Biometrika, Biometrika Trust, vol. 91(3), pages 559-578, September.
    2. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    3. Richard Scheines & Herbert Hoijtink & Anne Boomsma, 1999. "Bayesian estimation and testing of structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 64(1), pages 37-52, March.
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