Latent Interactions in Cross-Classified Structural Equation Modeling With Small to Moderate Samples

• In Event: Methodological Issues and Applications in Multilevel Modeling

Abstract

Despite the flexibility of multilevel structural equation modeling, a practical limitation researchers encounter is how to estimate interactions between latent variables with typical sample sizes when there are several levels of (potentially cross-classified/disparate) nesting. We develop a method-of-moments-corrected maximum likelihood estimator for models with latent interactions in cross-classified SEMs that is well-suited to the small-to-moderate samples typically seen in prospective education research. We probe the consistency, variability and convergence of the estimator with small-to-moderate samples. The estimator emerges as a practical alternative/complement to maximum likelihood (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 the [BLINDED-for-review] R package and are illustrated through an example.

Authors

• Benjamin Kelcey, University of Cincinnati
  Presenting Author

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

• Fangxing Bai, University of Cincinnati
  Non-Presenting Author