Multiple Imputation with Structural Equation Modeling: Using auxiliary variables when data are missing

Even very well-designed, well-executed research can result in missing responses at any rate, particularly in survey research. This Monte Carlo study investigated the effectiveness of the inclusive strategy with incomplete data, in a structural equation modeling framework with multiple imputation. Specifically, the study examined the influence of sample size, missing rates, various missingness mechanism combinations, and the inclusive strategy on convergence failure, bias, standard error, and confidence interval coverage of parameters, and model fit. The inclusive strategy, which includes additional variables in the imputation model, was found to improve parameter estimation in most cases, particularly with the convex type of missingness and the nonignorable cases caused by MAR(missing at random) and the restrictive strategy. Implications and future directions are discussed. SAS macro programs are attached.

Dr. Jin Eun Yoo is an associate professor at Korea National University of Education (major: educational measurement, evaluation, and statistics). She received her Ph. D. from Purdue University, and worked as a psychometrician with Pearson (location: Austin, TX). Her research interests include methodological issues in applied statistics.