Structural After Measurement Estimation With Missing Data in Structural Equation Models

• In Event: Factor Analysis and Structural Equation Modeling in Educational Research

Abstract

We develop a structural after measurement (SAM) method for structural equation modeling (SEMing) that accommodates missing data and probe the degree to which the method can retain key advantages that have been previously established. The results show that the proposed SAM missing data estimator emerges as a useful estimator when data are missing in small to moderate samples or in large samples with significant missing data. It outperforms conventional full information estimators in terms of convergence, bias, error variance, and power. The proposed estimator is implemented in R and are illustrated through a simple mediation example probing the degree to which the relationship between teacher knowledge and instruction is mediated by teaching self-efficacy.

Authors

• Benjamin Kelcey, University of Cincinnati

• Fangxing Bai, Montana State University

• Kyle T. Cox, University of North Carolina - Charlotte

• Yanli Xie, Florida State University

• Amota Ataneka, University of Cincinnati