A Simple Configural Approach for Testing Person-Oriented Mediation Hypotheses

Statistical methods to test hypotheses about direct and indirect effects from a person-oriented research perspective are scarce. For categorical variables, previously suggested approaches use configural frequency analysis (CFA) to detect extreme patterns (CFA Types/Antitypes) that are responsible for the observed direct and indirect effects. Existing methods rest on complex (log-linear) model comparison strategies and may perform poorly with respect to Type I error protection and statistical power. We, therefore, propose a simplified configural approach to answer the question “What carries a mediation process?” This simplified approach is based on two log-linear models that are needed to estimate (variable-oriented) direct and indirect effects. The first model identifies extreme patterns for the predictor-mediator path, the second model searches for extreme cells in the mediator-outcome path. Joint significance testing can be used to test the presence of mediation. Definitions of Mediation Types/Antitypes are given based on possible Type/Antitype patterns for the binary simple mediation model. In two Monte-Carlo simulation experiments, we evaluate the performance of the simplified approach in a homogenous population (i.e., where all individuals develop homogenously along a variable-oriented mediation mechanism) and a heterogenous population (i.e., where specific configurations, instead of a variable-oriented effect, drive the mediation process). Results suggest that the presented approach performs acceptably with respect to Type I error protection and statistical power. In general, larger sample sizes are preferable to reliably detect mediation-generating configurations. An empirical example is given for illustrative purposes and extensions and limitations of the proposed method are discussed.