Impact of missing data and ICC on full information maximum-likelihood estimation in multilevel SEMs

Abstract

A Monte Carlo simulation study was conducted to investigate the performance of full information maximum-likelihood (FIML) estimator in multilevel structural equation modeling (SEM) with missing data and different intra-class correlations (ICCs) coefficients. The study simulated the influence of two independent variables (missing data patterns, and ICC coefficients) in multilevel SEM on five outcome measures (model rejection rates, parameter estimate bias, standard error bias, coverage, and power). Results indicated that FIML parameter estimates were generally robust for data missing on outcomes and/or higher-level predictor variables under the data completely at random (MCAR) and for data missing at random (MAR). However, FIML estimation yielded substantially lower parameter and standard error bias when data was not missing on higher-level variables, and in high rather than in low ICC conditions (0.50 vs 0.20). Future research should extend to further examination of the impacts of data distribution, complexity of the between-level model, and missingness on the between-level variables on FIML estimation performance.