Prior sensitivity analysis in default Bayesian structural equation modeling

S.J. van Erp, J. Mulder, Daniel L. Oberski

Research output: Contribution to journalArticleScientificpeer-review

17 Citations (Scopus)
93 Downloads (Pure)

Abstract

Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models and solve some of the issues often encountered in classical maximum likelihood estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information. However, the prior can have a serious influence on the estimation of the model parameters, which affects the mean squared error, bias, coverage rates, and quantiles of the estimates. In this article, we investigate the performance of three different default priors: noninformative improper priors, vague proper priors, and empirical Bayes priors-with the latter being novel in the BSEM literature. Based on a simulation study, we find that these three default BSEM methods may perform very differently, especially with small samples. A careful prior sensitivity analysis is therefore needed when performing a default BSEM analysis. For this purpose, we provide a practical step-by-step guide for practitioners to conducting a prior sensitivity analysis in default BSEM. Our recommendations are illustrated using a well-known case study from the structural equation modeling literature, and all code for conducting the prior sensitivity analysis is available in the online supplemental materials.

Original languageEnglish
Pages (from-to)363-388
JournalPsychological Methods
Volume23
Issue number2
DOIs
Publication statusPublished - 2018

Keywords

  • Bayesian
  • CLASS SEPARATION
  • COMPONENTS
  • EMPIRICAL BAYES
  • FRAMEWORK
  • FREQUENTIST
  • INFERENCE
  • LINEAR MIXED MODELS
  • PARAMETERS
  • PRIOR DISTRIBUTIONS
  • SELECTION
  • default priors
  • sensitivity analysis
  • structural equation models

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