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A Bayesian Approach for Estimating Mediation Effects With Missing Data


Abstract 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.
Created Date 2013
Contributor Enders, Craig (ASU author) / Fairchild, Amanda J. (Author) / MacKinnon, David (ASU author) / College of Liberal Arts and Sciences / Department of Psychology
Series MULTIVARIATE BEHAVIORAL RESEARCH
Type Text
Extent 48 pages
Language English
Identifier DOI: 10.1080/00273171.2013.784862 / ISSN: 0027-3171 / ISSN: 1532-7906
Copyright
Reuse Permissions All Rights Reserved
Citation 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
Note "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."
Collaborating Institutions ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


  A Bayesian Approach for Estimating Mediation Effects With Missing Data
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