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# How to interpret model with “main effect 1 + main effect 2 + main effect 1*main effect 2"?

Hello everyone,

I have got a model which has two independent variables (IV):
Group: Explorers vs. Time Controls (between-subjects)
Season: Summer 1, Autumn, Winter, Spring, Summer 2 (within-subjects)

In JASP's Bayesian RM ANOVA this shows me that this model has the most support:
Group + Season + Group*Season (BF10=870).

The closest single main effect model is Season (BF=190).

How do I interpret the Group + Season + Group*Season model correctly?

And I've read Part I and Part II of the JASP papers for psychology but I'm not sure how to make sense of this model.

Best regards
eniseg2

• Hi Eniseg2,

Well, the model with the interaction is preferred over the other models. Compared to the model with only Season, the BF = 870/190 = 4.58. Maybe it also helps to select "Compare to best model" and set the Bayes factor display to BF_01 (instead of BF_10): this will tell you how much more support the data provide for the best model compared to each of the other models.

Cheers,
E.J.

• Hi E.J.
Yes, I have understood that. But what I'm wondering how to explain what the model means. If you have a single main effect model, you can say 'Season affects Mood' for example. If it's an interaction you can say 'Season affects Mood depending on Group'. But what can I say with this model which has both main effects and the interaction in it?

Thank you very much,
Eniseg2

• I'd say you interpret the interaction, as you propose. JASP respect the principle of marginality, so models with the interaction automatically also include the constituent main effects.
E.J.