Statistical Considerations in Identifying Mechanisms of Change

The statistical search for mechanisms of change involves multiple inferential tests, ones that generally follow a fixed sequence designed to demonstrate mediation. While there are several popular approaches to conducting such tests, e.g., SEM and MRA, the inflated Type I error rate problem associated with conducting these tests has received little, if any, attention. This paper offers 2 solutions to avoid committing Type I errors associated with mediational tests. Most straightforward, investigators may choose to use a Bonferroni adjustment. In contrast, a design‐based approach can be used that tests rival explanations for the observed effects. Examples drawn from addiction research are provided.