What mediation analysis can (not) do

The present article is concerned with a common misunderstanding in the interpretation of statistical mediation analyses. These procedures can be sensibly used to examine the degree to which a third variable ( Z) accounts for the influence of an independent ( X) on a dependent variable ( Y) conditional on the assumption that Z actually is a mediator. However, conversely, a significant mediation analysis result does not prove that Z is a mediator. This obvious but often neglected insight is substantiated in a simulation study. Using different causal models for generating Z (genuine mediator, spurious mediator, correlate of the dependent measure, manipulation check) it is shown that significant mediation tests do not allow researchers to identify unique mediators, or to distinguish between alternative causal models. This basic insight, although well understood by experts in statistics, is persistently ignored in the empirical literature and in the reviewing process of even the most selective journals. ► Mediation analysis is a state of the art tool in scientific analysis. ► However, there are common misunderstandings about what mediation analysis can do and what it cannot do. ► Our simulation results high mediation mimicry: significant results may reflect other causal models than mediation. ► The crucial message is that mediation analysis is not suitable to identifying effective mediators. ► It is only suitable to test the significance of an assumed causal mediator.