Structural After Measurement Estimation of n-Level Structural Equation Models

• In Event: Longitudinal and Structural Equation Modeling

Abstract

Despite the flexibility of multilevel structural equation modeling (MLSEM), a practical limitation is how to effectively estimate model parameters 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 (i.e., arbitrary number of levels of nesting) that is well-suited to the types of small to moderate sample sizes typically seen in prospective education research. We then 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 maximum likelihood (ML) because it often outperforms ML in small to moderate n-level samples in terms of convergence, bias, and variance.

Author

• Benjamin Kelcey, University of Cincinnati
  Presenting Author