Croon's Estimation of Multilevel Structural Equation Models With Partially Nested Data

• In Event: Methods to Account for Cluster Dependencies and Subgroup Differences in Structural Equation Modeling

Abstract

This study extends and examines a structure after measurement approach with a Croon’s correction (SAM-Croon’s) to estimate multilevel structural equation models (MLSEM) when data are partially nested. MLSEMs estimated using maximum likelihood appropriately reflect the complex multilevel systems and latent variables common in educational research. A crucial requirement for maximum likelihood estimation is a large sample size rendering it inappropriate in many planned educational research studies. However, SAM-Croon’s has demonstrated unbiased and efficient estimates for MLSEMs even when sample sizes are limited. We extend this promising estimator to MLSEMs with data that are partially nested and use simulation studies to demonstrate its effectiveness as an alternative to typical full information maximum likelihood estimation.

Authors

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

• Benjamin Kelcey, University of Cincinnati
  Non-Presenting Author

• Fangxing Bai, University of Cincinnati
  Non-Presenting Author