A Comparison of Imputation Methods for Bayesian Factor Analysis Models
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.
Từ khóa
Tài liệu tham khảo
Aitkin M., 2005, Contemporary psychometrics: A festschrift for Roderick P. McDonald, 207
Holzinger K. J., 1939, A study of factor analysis: The stability of a bi-factor solution (No. 48)
Jennrich R. I., 2006, Presented at the 71st annual meeting of the Psychometric Society
Little R. J. A., 1987, Statistical analysis with missing data
Thomas A., 2006, R News, 6, 12
van Buuren S., Journal of Statistical Software
Wothke W., 2000, Modeling longitudinal and multilevel data: Practical issues, applied approaches, and specific examples