The purpose of this study is to investigate the effects of missing data techniques in SEM under different multivariate distributional conditions. Using Monte Carlo simulations, this research examines the performance of four missing data methods in SEM: full information maximum likelihood (FIML), expectation maximum (EM) procedure, multiple imputation (MI), and similar response pattern imputation (SRPI) in the missing data mechanisms of missing completely at random (MCAR) and missing at random (MAR). The effects of three independent variables (sample size, missing proportion, and distribution shape) are investigated on parameter and standard error estimation, standard error coverage and model fit statistics. An inter-correlated 3-factor CFA model is used. The findings of this study indicate that FIML is the most robust method in terms of parameter estimate bias; FIML and MI generate almost equally accurate standard error coverage; and MI is the best in terms of estimation efficiency/accuracy and model rejection rate.
The results of SRPI in this study are consistent with previous studies in the literature. Generally speaking, SRPI produces unbiased parameter estimates for factor loadings and factor correlations under MCAR. However, when there are severe missingness or nonnormality conditions in the data or when the sample size is very small, it has bias problems on error variance estimates for indicators with moderate to low factor loadings. Some of the merits regarding SRPI found in this study are that it is more efficient than FIML under MCAR when data not only have small to moderate missingness, but also are severely nonnormal; it was also found to be more efficient for factor loading estimates of those indicators with missing data in MAR when the missing percentage is high and the nonnormality condition is most severe.
Recommendations regarding when to use each of the missing data techniques are provided at the end of the study. Future works are also discussed for improvement after this research.