Mediating variables are prominent in psychological theory and research. A mediating variable transmits the effect of an independent variable on a dependent variable. Differences between mediating variables and confounders, moderators, and covariates are outlined. Statistical methods to assess mediation and modern comprehensive approaches are described. Future directions for mediation analysis are discussed.One of the primary reasons for the popularity of mediating variables in psychology is the historical dominance of the stimulus organism response model (Hebb 1966). In this model, mediating mechanisms in the organism translate how a stimulus leads to a response. A second related reason for the importance of mediating variables is that they form the basis of many psychological theories. For example, in social psychology, attitudes cause intentions, which then cause behavior (Fishbein & Ajzen 1975), and in cognitive psychology, memory processes mediate how information is transmitted into a response. A newer application of the mediating variable framework is in prevention and treatment research, where interventions are designed to change the outcome of interest by targeting mediating variables that are hypothesized to be causally related to the outcome. A third reason for interest in mediation is methodological. Mediation represents the consideration of how a third variable affects the relation between two other variables. Although the consideration of a third variable may appear simple, three-variable systems can be very complicated, and there are many alternative explanations of observed relations other than mediation. This methodological and statistical challenge of investigating mediation has made methodology for assessing mediation an active research topic.
This review first defines the mediating variable and the ways in which it differs from other variables, such as a moderator or a confounder. Examples of mediating variables used in psychology are provided. Statistical methods to assess mediation in the single-mediator case are described, along with their assumptions. These assumptions are addressed in sections describing current research on the statistical testing of mediated effects, longitudinal mediation models, models with moderators as well as mediators, and causal inference for mediation models. Finally, directions for future research are outlined.
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