Bayesian multilevel structural equation modeling: An investigation into robust prior distributions for the doubly latent categorical model

Sara Van Erp*, William J Browne

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Bayesian estimation of multilevel structural equation models (MLSEMs) offers advantages in terms of sample size requirements and computational feasibility, but does require careful specification of the prior distribution especially for the random effects variance parameters. The traditional “non-informative” conjugate choice of an inverse Gamma prior with small hyperparameters has been shown time and again to be problematic. In this paper, we investigate alternative, more robust prior distributions. In contrast to multilevel models without latent variables, MLSEMs have multiple random effects variance parameters, both for the multilevel structure and for the latent variable structure. It is therefore even more important to construct reasonable priors for these parameters. We find that, although the robust priors outperform the traditional inverse-Gamma prior, their hyperparameters do require careful consideration.
Original languageEnglish
JournalStructural Equation Modelling: A Multidisciplinary Journal
Early online date21 Jun 2021
DOIs
Publication statusE-pub ahead of print - 21 Jun 2021

Bibliographical note

Publisher Copyright:
© 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.

Structured keywords

  • SoE Centre for Multilevel Modelling

Keywords

  • Bayesian
  • robust priors
  • multilevel models

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