Structural After Measurement Estimation of (Non)Linear n-Level Structural Equation Models With Cross-Classification Structures

• In Event: Saturday Roundtable Session 11:25 am
  In Roundtable Session: Issues and Developments in Structural Equation Modeling (Table 10)

Abstract

Despite the flexibility of multilevel structural equation modeling (MLSEM), a practical limitation many researchers encounter is how to effectively estimate model parameters and latent interactions with typical sample sizes when there are many levels of (potentially disparate) nesting. We develop a method-of-moments corrected maximum likelihood estimator for n-level SEMs well-suited to the types of small-to-moderate sample sizes typically seen in education research. We probe the consistency, variability, and convergence of the estimator with small-to-moderate n-level samples. The estimator emerges as a practical alternative or complement to conventional ML because it often outperforms ML in small-to-moderate n-level samples in terms of convergence, bias, and variance. The proposed estimators are implemented in R and are illustrated through an n-level teacher development example.

Authors

• Fangxing Bai, Montana State University

• Benjamin Kelcey, University of Cincinnati