Psychological Methods· 2013Q1
Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros.
- 2,144citations
- Q1SCImago
- 2013year
Short summary
This paper extends counterfactual mediation analysis to handle dichotomous mediators and count outcomes, enabling causal interpretation even with exposure-mediator interactions.
AI-generated from the title and abstract; the full text is not read.
Key points
- Extends counterfactual mediation analysis to dichotomous mediators and count outcomes.
- Enables causal interpretation of mediation in the presence of exposure-mediator interactions.
- Compares inferences possible with counterfactual versus standard statistical approaches.
- Provides SAS and SPSS macros for implementing the described mediation analysis techniques.
AI-generated from the title and abstract; the full text is not read.
Abstract
Mediation analysis is a useful and widely employed approach to studies in the field of psychology and in the social and biomedical sciences. The contributions of this article are several-fold. First we seek to bring the developments in mediation analysis for nonlinear models within the counterfactual framework to the psychology audience in an accessible format and compare the sorts of inferences about mediation that are possible in the presence of exposure-mediator interaction when using a counterfactual versus the standard statistical approach. Second, the work by VanderWeele and Vansteelandt (2009, 2010) is extended here to allow for dichotomous mediators and count outcomes. Third, we provide SAS and SPSS macros to implement all of these mediation analysis techniques automatically, and we compare the types of inferences about mediation that are allowed by a variety of software macros.
The authors' abstract, as published at the source. Psychological Methods, 2013 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics