Mediation

Purpose:

To assess a third or "black-box" variable in the middle of a casual relationship. To test a hypothetical cause chain where the variable X (IV) effects a second variable M, and in turn, that variable affects a third variable Y (DV).


Context used:


Jamovi Walkthrough:


Output Interpretation:

p-value: The probability you detect a meaningful relationship/difference when there is none. Looking for a small value (p < 0.5).

If p < 0.5, reject the null hypothesis. It IS a significant effect.

If p > 0.5, accept the null hypothesis. It is NOT a significant effect.

% mediation: How much of the relationship the effect accounts for.

If % is higher for an indirect effect, there is a higher effect of exposure (IV) on the outcome (IV) through the mediator.

If % is higher for a direct effect, there is a higher effect of exposure (IV) on the outcome (DV) absent the mediator.

If % is split between indirect and direct effect, it is a partial mediation.

If % is not split between indirect and direct effect, there is a full mediation (Note: this rarely happens in practice)


APA Format:

Appropriate data visualization: Path model or Table


Sample table: See below


Sample write-up:

See https://www.starcresto.com/blog/mediation-spss for a sample APA format write-up.


Or, see the image below. (Note: The analysis in the image was completed in SPSS rather than Jamovi, but the values and interpretation remian the same, and the output format is similar enough to follow along).

Jamovi tutorial video for mediation:

Mediation Tutorial.mp4