After a significant one-way ANOVA in SPSS the real work begins. There needs to be clarity between which groups differences, in regard to the dependent variable exists.
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You will do post-hoc-tests which are essentially pairwise comparisons of all the groups you have in your sample. For the one-way ANOVA this means doing t-tests. However, it is necessary to counter alpha-error inflation (and draw false positive conclusions) when testing multiple times on the same groups.
Thankfully, SPSS has a routine that requires only a few clicks to request the appropriate results, even corrected for alpha-error inflation.
I will show how to interpret the results and utter some words of caution for a significant one-way 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
0:13 Using pairwise t-tests and alpha error correction
0:45 Interpreting post-hoc results
1:14 Words of caution about p-value and power
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