Mediationexplaining

Mediationexplaining is a neologism used in scholarly and educational contexts to describe the practice of explaining mediation, the process by which an independent variable influences a dependent variable through a mediator. The term is not standardized and does not denote an established field; rather, it signals a focus on clarity, pedagogy, and transparent interpretation of mediation analyses.

In statistics and social science research, mediationexplaining involves describing the theoretical rationale for mediation, detailing model specification, and communicating results in accessible terms. It often includes outlining the assumptions underlying mediation analysis (such as no unmeasured confounding and correct temporal ordering), presenting estimates of indirect effects, and using resampling methods like bootstrapping to provide confidence intervals. The aim is to help readers understand how mediated effects arise and how robust they are to different modeling choices.

Practitioners may use visuals such as causal-path diagrams, step-by-step explanations, and comparisons of total, direct, and indirect effects to enhance understanding. In educational settings, mediationexplaining helps students distinguish mediation from moderation and from simple correlation, and it emphasizes transparent reporting and interpretation rather than solely focusing on statistical significance.

Limitations of the term include its nonstandard status and potential ambiguity about scope, as well as the reliance on statistical assumptions that may be violated in practice. When used, authors should define the concept clearly to avoid misinterpretation and ensure consistent communication across disciplines.

Related topics include mediation analysis, causal mediation, Baron and Kenny’s approach, Hayes’ methods, and bootstrapping for indirect effects.