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.
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- Enders, Craig (Contributor)
- Fairchild, Amanda J. (Author)
- MacKinnon, David (Contributor)
- College of Liberal Arts and Sciences (Contributor)
- 2013
- Digital object identifier: 10.1080/00273171.2013.784862
- Identifier TypeInternational standard serial numberIdentifier Value0027-3171
- Identifier TypeInternational standard serial numberIdentifier Value1532-7906
- "This is an Author's Accepted Manuscript of an article published in Multivariate Behavioral Research, 48(3), 340-369 2013 copyright Taylor & Francis, available online at: http://www.tandfonline.com/doi/abs/10.1080/00273171.2013.784862.", opens in a new window
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Enders, C. K., Fairchild, A. J., & MacKinnon, D. P. (2013). A bayesian approach for estimating mediation effects with missing data. Multivariate Behavioral Research, 48(3), 340-369. doi:10.1080/00273171.2013.784862