BIC and Alternative Bayesian Information Criteria in the Selection of Structural Equation Models

Kenneth A. Bollen, Jeffrey J. Harden, Surajit Ray, Jane Zavisca

Research output: Contribution to journalArticle

50 Scopus citations

Abstract

Selecting between competing structural equation models is a common problem. Often selection is based on the chi-square test statistic or other fit indices. In other areas of statistical research Bayesian information criteria are commonly used, but they are less frequently used with structural equation models compared to other fit indices. This article examines several new and old information criteria (IC) that approximate Bayes factors. We compare these IC measures to common fit indices in a simulation that includes the true and false models. In moderate to large samples, the IC measures outperform the fit indices. In a second simulation we only consider the IC measures and do not include the true model. In moderate to large samples the IC measures favor approximate models that only differ from the true model by having extra parameters. Overall, SPBIC, a new IC measure, performs well relative to the other IC measures.

Original languageEnglish (US)
Pages (from-to)1-19
Number of pages19
JournalStructural Equation Modeling
Volume21
Issue number1
DOIs
StatePublished - Jan 1 2014

Keywords

  • BIC
  • Bayes factor
  • chi-square tests
  • model fit
  • model selection
  • structural equation models

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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