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Conditional Process Analysis for Two-Instance Repeated-Measures Designs
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DOI：10.1037/met0000715.png)
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Abstract
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Models where some part of a mediation is moderated (conditional process models) are commonly used in psychology research, allowing for better understanding of when the process by which a focal predictor affects an outcome through a mediator depends on moderating variables. Methodological developments in conditional process analysis have focused on between-subject designs. However, two-instance repeated-measures designs, where each subject is measured twice: once in each of two instances, are also very common. Research on how to statistically test mediation, moderation, and conditional process models in these designs has lagged behind. Judd et al. (2001) introduced a piecewise method for testing for mediation, that Montoya and Hayes (2017) then translated to a path-analytic approach, quantifying the indirect effect. Moderation analysis in these designs has been described by Judd et al. (2001, 1996), and Montoya (2018). The generalization to conditional process analysis remains incomplete. I propose a general conditional process model for two-instance repeated-measures designs with one moderator and one mediator. Simplifications of this general model correspond to more commonly used moderated mediation models, such as first-stage and second-stage conditional process analysis. An applied example shows both how to conduct the analysis using MEMORE, a free and easy-to-use macro for SPSS and SAS, and how to interpret the results of such an analysis. Alternative methods for evaluating moderated mediation in two-instance repeated-measures designs using multilevel approaches are also discussed.
Keywords:
repeated-measures designs
linear regression
multilevel models
longitudinal analysis
structural equation modeling
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