After a significant repeated measures ANOVA in SPSS the real work begins. There needs to be clarity between which points in time differences in regard to the dependent variable exist. Post-hoc-tests will help in further investigating effects.
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Post-hoc-tests are essentially pairwise comparisons of all the groups you have in your sample. For the repeated measures ANOVA this means doing dependent t-tests. However, it is necessary to counter alpha-error inflation (and draw false positive conclusions) when testing multiple times on the same groups.

I will therefore show how to do pairwise comparisons using the EM means (estimated marginal means) function within the repeated measures ANOVA in SPSS. I will also apply Bonferroni correction since the choices are limited here. If you want different alpha error corrections than Bonferroni and Sidak, you might want to go with R: [ Ссылка ]

Afterwards I will interpret the results and utter some words of caution for a significant repeated measures ANOVA and non-significant post-hoc-tests as well as why not to use post-hoc power analysis.


📚 Literature:
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- Hoenig, J. M., & Heisey, D. M. (2001). The abuse of power: the pervasive fallacy of power calculations for data analysis. The American Statistician, 55(1), 19-24.
- Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129-133.


⏰ Timestamps:
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0:00 Introduction
0:17 Pairwise t-tests
1:15 Interpreting post-hoc results
1:48 Words of caution about p-value and power


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