Wishart Hierarchical Models for Meta-Analytic Latent Variable Models: A Demonstration With the Hospital Anxiety and Depression Scale

• In Event: AERA Virtual Poster Session
  In Virtual Poster Session: SIG-Multilevel Modeling Virtual Poster Session

Abstract

I present an approach to meta-analytic SEM that relies on hierarchical modeling of sample covariance matrices under the assumption that the matrices are Wishart. The approach handles the commonplace fixed- and random- effects meta-analytic SEMs, and solves the problem of dependent covariance matrices where more than one covariance matrix is obtained from a single study. The estimation approach is Bayesian, and I provide some guidance on prior specification, as well as model code to aid application and further study of the approach. Finally, I demonstrate the approach with 28 correlation matrices collected from 21 studies of the Hospital Anxiety and Depression scale.

Author

• James O. Uanhoro, University of North Texas
  Presenting Author