Handling Missing Data in Structural Equation Models in R. A Replication Study for Applied Researchers

Abstract

Introduction. Multiple imputation (MI) is one of the most highly recommended methods for replacing missing values in research data. The scope of this paper is to demonstrate missing data handling in SEM by analyzing two modified data examples from educational psychology, and to give practical recommendations for applied researchers.Method. We provide two examples (N = 589 and N = 621, respectively) based on previous studies of students’ self-concepts, mastery goals and performance avoidance goals, and a 7- step tutorial. Then, we produced 20% and 40% missing data under three missing mechanisms by these complete, genuine data sets. The resulting datasets were then analyzed by (1) listwise deletion and structural equation models (SEM), (2) full information maximum likelihood (FIML) with SEM, and (3) MI combined with SEM and pooling. Thus, the results stem from 2 × 3 × 3 conditions.Results. Previous research was replicated by illustrating a practical way to combine MI with SEM and pooling. The assumed factor structure was depicted in both examples with multiply imputed values applied.Discussion. We suggest adding variables to clarify the missing data mechanism, especially for dependent variables as motivation. Such variables might indicate whether missing values in dependent variables are correlated with independent variables (e.g., interest) or the dependent variable itself (e.g. lack of motivation independently of interest).