Mediation and Moderation Analysis AI Prompt

Design and analyze a mediation or moderation analysis for my study.

Conceptual model: {{conceptual_model}} (e.g. 'X → M → Y' or 'X × W → Y')
Hypotheses: {{hypotheses}}
Data available: {{data}}

1. Clarify the conceptual question:

   Mediation (X → M → Y):
   - Asks: does X affect Y through its effect on M?
   - M is the mechanism through which X influences Y
   - Example: does a training intervention (X) improve job performance (Y) by increasing self-efficacy (M)?

   Moderation (X × W → Y):
   - Asks: does the effect of X on Y depend on the level of W?
   - W is the boundary condition that strengthens or weakens the X-Y relationship
   - Example: does the training effect on performance (X → Y) differ by employee tenure (W)?

   Moderated mediation (the indirect effect is moderated):
   - Asks: does the mediated pathway (X → M → Y) operate differently at different levels of W?

2. Mediation analysis — using Hayes PROCESS or lavaan:

   Requirements:
   - Temporal precedence: X must precede M must precede Y in time
   - Causal inference requires ruling out reverse causation and confounding of M-Y relationship
   - Distinguish between mediation (mechanism) and moderation (boundary condition)

   Steps:
   a. Test total effect of X on Y (path c)
   b. Test effect of X on M (path a)
   c. Test effect of M on Y controlling for X (path b)
   d. Test direct effect of X on Y controlling for M (path c')
   e. Calculate indirect effect = a × b with bootstrap CI (5000 bootstraps; 95% CI not crossing 0 = significant mediation)

   Note: Baron and Kenny's causal steps approach (requiring significant c path) is outdated. Use bootstrap indirect effects.

3. Moderation analysis:
   - Center continuous predictors before computing interaction terms
   - Test the interaction term (X × W)
   - If significant: probe the interaction with simple slopes at W = mean ± 1SD (or for binary W, at each level)
   - Plot the interaction: fitted values of Y across the range of X, separately for levels of W
   - Report: interaction coefficient, simple slopes with SEs and p-values, region of significance (Johnson-Neyman technique)

4. Common errors to avoid:
   - Do not interpret partial mediation as a finding — it is just incomplete mediation
   - Do not conclude mediation from cross-sectional data without acknowledging this is assumed, not demonstrated
   - Do not probe an interaction that is not statistically significant

Return: analysis code, results interpretation, interaction plot, and a methods paragraph.