Abstract
Conditional process modeling is a data analytical approach that combines statistical mediation and moderation analysis. Mediation analysis is used to uncover the mechanisms that underlie or explain a causal effect. It is well suited to statistically model causal processes that take place between a predictor variable and an outcome variable. Moderation analysis investigates whether an effect of a predictor variable on an outcome variable varies under different conditions or for different individuals. Conditional process models (also referred to as moderated mediation models or mediated moderation models) combine the ideas of mediation and moderation. Thus they are well suited to investigate how processes between cause and outcome vary depend on characteristics of the individuals or their contexts. This article introduces the foundations of mediation, moderation, and conditional process analysis. Statistical estimation, testing, and interpretation are covered for simple linear models. References regarding more advanced models are provided.
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