A Structural After-Measurement Approach for Structural Equation Models With Latent Interactions and Missing Data

• In Event: AERA Roundtable Session Sunday 8:00 am Four Seasons Ballroom 2-3
  In Roundtable Session: Applications and Advances in Structural Equation Modeling (Table 6)

Abstract

Interactions between a focal independent variable and another variable (i.e., moderator) have long been an important consideration for educational researchers with interactions often occurring between latent variables. Unfortunately, the computational complexity of estimating latent interactions has limited their utilization. The purpose of this study is to develop a structural after measurement (SAM) estimator for the analysis of structural equation models (SEMs) that include latent interactions but have missing data. Results include four SAM approaches that all accommodate latent interactions and missing data. Subsequently, a series of Monte Carlo simulation studies was conducted to investigate the performance of the different SAM estimator approaches with a single latent interaction, multiple latent interactions, and latent moderated mediation.

Authors

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

• Benjamin Kelcey, University of Cincinnati

• Onima Vadakke Thodiyil, University of North Carolina - Charlotte