PROCESS : A Versatile Computational Tool for Mediation , Moderation , and Conditional Process Analysis

Abstract

Statistical mediation and moderation analysis are widespread throughout the behavioral sciences. Increasingly, these methods are also being integrated in the form of the analysis of ―mediated moderation‖ or ―moderated mediation,‖ or what Hayes (2013; Hayes & Preacher, 2012a) calls conditional process modeling. After a broad overview of some of the basic concepts of mediation analysis, moderation analysis, and conditional process modeling, I introduce PROCESS, a versatile modeling tool freely-available for SPSS and SAS that integrates many of the functions of existing and popular statistical tools for mediation and moderation analysis published in this journal and elsewhere into one easy-to-use procedure. PROCESS uses an ordinary least squares or logistic regression-based path analytical framework for estimating direct and indirect effects in simple and multiple mediator models, two and three way interactions in moderation models along with simple slopes and regions of significance for probing interactions, conditional indirect effects in moderated mediation models with a single or multiple mediators and moderators, and indirect effects of interactions in mediated moderation models also with a single or multiple mediators. Bootstrap methods are implemented for inference about indirect effects in both unmoderated as well as moderated mediation and mediated moderation models. Examples of the use of PROCESS are provided, and some of its additional features as well as some limitations are described.