Välitysvaikutusmallien

Välitysvaikutusmallit, known in English as mediation effect models or simply mediation models, are statistical tools used in social sciences and psychology to understand the indirect relationship between an independent variable and a dependent variable. Instead of a direct link, mediation models propose that the independent variable influences the dependent variable through a third variable, called the mediator. This mediator acts as a channel or mechanism through which the effect is transmitted.

The core idea of a mediation model is to examine if the relationship between X (independent variable) and Y (dependent variable) is explained by M (mediator). This is typically assessed by testing two conditions: first, that X is related to M, and second, that M is related to Y, controlling for X. If both conditions hold, and the direct effect of X on Y becomes non-significant or significantly weaker when M is included in the model, it provides evidence for mediation.

Several statistical methods can be employed to test mediation, including regression analysis (e.g., Baron and Kenny's approach), structural equation modeling (SEM), and bootstrapping. Bootstrapping is often preferred as it provides more robust estimates of indirect effects and their confidence intervals, especially in smaller sample sizes. Understanding mediation is crucial for developing effective interventions, as it helps identify specific points of intervention that can lead to desired outcomes. For example, in education, a model might explore how teaching methods (X) affect student engagement (M), which in turn influences academic performance (Y).