A Bayesian Approach for Estimating Mediation Effects With Missing Data

Craig K. Enders, Amanda J. Fairchild, David Mackinnon

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


Methodologists have developed mediation analysis techniques for a broad range of substantive applications, yet methods for estimating mediating mechanisms with missing data have been understudied. This study outlined a general Bayesian missing data handling approach that can accommodate mediation analyses with any number of manifest variables. Computer simulation studies showed that the Bayesian approach produced frequentist coverage rates and power estimates that were comparable to those of maximum likelihood with the bias-corrected bootstrap. We share an SAS macro that implements Bayesian estimation and use 2 data analysis examples to demonstrate its use.

Original languageEnglish (US)
Pages (from-to)340-369
Number of pages30
JournalMultivariate Behavioral Research
Issue number3
StatePublished - May 2013

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)


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