I want to test if the interaction effect is contributed by a possible mediator. I have found a significant A(A1 vs A2)*B (B1 vs B2) interaction effect on Y, and I want to test if this interaction effect on Y is mediated by M, how to run the analysis? Is this correct to run one by one? e.g., In A1 condition, testing B--M--Y, and then in A2 condition, testing B--M--Y.
am attaching 2 articles with mediation and moderation/interaction, hope you find them useful.
The book Introduction to Mediation, Moderation, and Conditional Process Analysis by Andrew F, Hayes is an excellent source. It will guide you through the process from simple models to more sophisticated ones. Hayes developed a program, run with SPSS, but his module is free, you just download from his page.
Hello
1. The Basic Mediation Model is,
M = β0 +β 1 X=e.
2. Indirect (mediated) effect of X on Y = a*b and Direct (unmediated) effect of X on Y = c’ .
3. To test for mediation we can examine the statistical significance of the indirect effect (ie. H0 : ab = 0).
4. There are many statistical tests of the indirect effect (see Hayes & Scharkow, 2013 for a review). Some of these tests (eg. the Sobel Z test) assume the indirect effect is normally distributed.
5. Unfortunately, the indirect effect is rarely normally distributed.
6. Non-parametric bootstrapping (with confidence intervals) has been recommended for testing mediation as it does assume the indirect effect is normally distributed and yields the most accurate results .
7. Please refer to,
Hayes, A. (2013). Introduction to mediation, moderation, and conditional process analysis. New York: Guilford Press.
Regards,
Zuhair
Figured it out. Melissa Simone's comment was right on spot.
here is a resource https://ademos.people.uic.edu/Chapter15.html
I have come across this resource but sounds like it doesn't support "treat" to be an interaction.
https://ademos.people.uic.edu/Chapter14.html#23_interpreting_barron__kenny_results
my sample code:
fitM
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