Bayesian Versus Maximum Likelihood Estimation of Multitrait–Multimethod Confirmatory Factor Models

Jonathan Lee Helm, Laura Castro-Schilo, Zita Oravecz

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

This article compares maximum likelihood and Bayesian estimation of the correlated trait–correlated method (CT–CM) confirmatory factor model for multitrait–multimethod (MTMM) data. In particular, Bayesian estimation with minimally informative prior distributions—that is, prior distributions that prescribe equal probability across the known mathematical range of a parameter—are investigated as a source of information to aid convergence. Results from a simulation study indicate that Bayesian estimation with minimally informative priors produces admissible solutions more often maximum likelihood estimation (100.00% for Bayesian estimation, 49.82% for maximum likelihood). Extra convergence does not come at the cost of parameter accuracy; Bayesian parameter estimates showed comparable bias and better efficiency compared to maximum likelihood estimates. The results are echoed via 2 empirical examples. Hence, Bayesian estimation with minimally informative priors outperforms enables admissible solutions of the CT–CM model for MTMM data.

Original languageEnglish (US)
Pages (from-to)17-30
Number of pages14
JournalStructural Equation Modeling
Volume24
Issue number1
DOIs
StatePublished - Jan 2 2017

All Science Journal Classification (ASJC) codes

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

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