Скачать или смотреть Two-way ANOVA in SPSS - effect size for pairwise comparisons - interaction effect

This tutorial shows how to calculate the effect size Cohen's d in the case of an interaction for the two-factorial ANOVA in SPSS.

First of all: this is about the pairwise comparisons for the interaction effect, i.e. whether in my fictitious example there are differences in income between the genders per experience level or between the experience levels per gender.

An effect size should also be calculated for the calculated pairwise comparisons that have a sufficiently small p-value. As these are pairwise comparisons, Cohen's d is used.

In this case, only independent t-tests need to be calculated, but this requires the use of filters and/or split outputs within SPSS. I will show this for my fictitious example where I could observe a sufficiently small p-value for the interaction effect.


Example in this video:
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The dependent variable is income. The first independent variable (factor I) is gender, the second independent variable (factor II) is experience. The aim is to test for a difference in terms of gender and experience. The special feature of the two-factor ANOVA is the interaction effect between these two, which is also tested for.


Literature
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📚 Cohen, J. (1992). Quantitive Methods in Psychology: A power primer. Psychological Bulletin, S. 155-159.

📚 Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond “p◀️ 0.05”. The American Statistician, 73(sup1), 1-19.


⏰ Timestamps:
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0:00 Introduction
0:52 Calculate Cohen's d (two factor levels)
2:52 Calculate Cohen's d (three+ factor levels)


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