Two-Condition Within-Participant Statistical Mediation Analysis: A Path-Analytic Framework

• Published in: Psychological methods Vol. 22; no. 1; pp. 6 - 27
• Main Authors: Montoya, Amanda K., Hayes, Andrew F.
• Format: Journal Article
• Language: English
• Published: United States American Psychological Association 01-03-2017
• Subjects: Effect Modifier, Epidemiologic; Humans; Models, Statistical; Monte Carlo Method; Path Analysis; Repeated Measures; Sampling (Experimental); Statistical Analysis; path analysis; mediation; resampling methods; indirect effect; within-participant design
• ISSN: 1082-989X; 1939-1463
• DOI: 10.1037/met0000086

Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of 2 different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this article we recast Judd et al.'s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path-analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al.'s method requires, because it relies only on an inference about the product of paths-the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path-analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses.