Missing Data Techniques for Factor Analysis Author: Hong-Long Wang(Department of Statistics, National Taipei University), Meng-Li Yang(Research Center for Humanities and Social Sciences, Academia Sinica), Chun-Ju Chen(Department of Statistics, National Taipei University), Ting-Hsiang Lin(Department of Statistics, National Taipei University)

Vol.&No.：Vol. 57, No. 1
Date：March 2012
Pages：29-50
DOI：10.3966/2073753X2012035701002

Abstract：

Factor analysis is frequently employed to analyze scales and questionnaires. However, when the proportion of missing data is high or the missing data are not random, the number of factors extracted can be biased. We used the Taiwan Education Panel Survey (TEPS) and constructed 5 data sets with different missing proportions to assess the effects of missingness on factor analysis imputation. Complete observed data were used as a baseline for comparison. We compared the 4 treatments: available case method (AC), the complete case method (CC), MCMC single imputation (MCMC), and step-wise logistic regression single imputation (LR). The results show that the higher the missing proportion, the greater the discrepancy between the covariance matrix of the constructed data set and that of the baseline. For the AC method, the higher the proportion of missing data, the more the number of extracted factors exceeds that of the baseline. The AC method possessed the largest bias in factor loadings. The bias in factor loading of the CC method increased as the missing portion also increased. Thus, we recommend not applying the list-wise deletion method for factor analysis when the missing proportion is 20% or more.

Keywords：TEPS, missing data, exploratory factor analysis, MCMC imputation, logistic regression imputation

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