Structural After Measurement Estimation of n-Level Structural Equation Models

• In Event: AERA Roundtable Session Wednesday 3:45 pm - Gold 3
  In Roundtable Session: Recent Developments in Multilevel Modeling: Model Estimation and Evaluation

Abstract

Despite the flexibility of multilevel structural equation modeling, 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 method-of-moments corrected maximum likelihood estimators for n-level SEMs that are 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 estimators emerge as practical alternatives or complements to conventional ML because they often outperform ML in small-to-moderate n-level samples in terms of convergence, bias, and variance. The proposed estimators are implemented in the R package and are illustrated through an n-level teacher development example.

Authors

• Fangxing Bai, Montana State University

• Benjamin Kelcey, University of Cincinnati