A Bayesian Approach for Estimating Mediation Effects With Missing Data
Description
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.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2013
Agent
- Author (aut): Enders, Craig
- Author (aut): Fairchild, Amanda J.
- Author (aut): MacKinnon, David
- Contributor (ctb): College of Liberal Arts and Sciences