mediationbased

Mediationbased is a term used in social science and related disciplines to describe approaches that foreground mediation processes in explaining the relationship between an independent variable and a dependent variable. In a mediationbased framework, observed effects are decomposed into direct effects and indirect effects that operate through one or more mediator variables. The central idea is that causal influence often travels along intermediary steps rather than through a single, unmediated path.

In practice, mediationbased models are used to test hypotheses about how interventions produce outcomes by altering mediators such as knowledge, attitudes, or behaviors. This approach supports the design of more effective policies and programs by identifying leverage points that mediate the intended effect.

Common methods used in mediationbased research include traditional mediation analysis with the causal steps approach and the Sobel test, bootstrap confidence intervals for indirect effects, and modern techniques from structural equation modeling and causal inference, including directed acyclic graphs and counterfactual frameworks. Researchers typically assess the size of mediation effects, the proportion of the total effect mediated, and potential sequential or moderated mediation.

Limitations of mediationbased analyses include the need for correct temporal ordering and measurement of mediators, potential unmeasured confounding, and sensitivity to model specification. When mediators are themselves influenced by unmeasured variables or feedback loops, attribution of effects to specific pathways can be challenging.

Related terms include mediation analysis, causal mediation, and structural equation modeling. The term mediationbased is used to emphasize processes that rely on mediated links as foundational explanatory mechanisms rather than solely direct associations.