McNemar's can be used as a post hoc test
Significant main effects for Cochran's Q need to be explained
Non-parametric tests like chi-square, fisher's exact test, Kruskal-Wallis, Cochran's Q, and Friedman's ANOVA do not have post hoc analyses to explain significant main effects. In order to conduct these post hoc anlayses, researchers have to resort to using subsequent non-parametric tests for two groups.
In a prior post, I explained how Mann-Whitney U tests were used in a post hoc fashion for significant main effects found with Kruskal-Wallis analyses. This is pertinent for between-subjects tests.
If you are using a within-subjects design with three or more observations of a dichotomous categorical outcome, you utilize Cochran's Q test to assess main effects. If a significant main effect is found, then McNemar's tests have to be employed for post hoc group comparisons. Significant post hoc tests (or relative risk calculations) will provide evidence of significant differences across observations or within-subjects.
Non-parametric statistics should be employed more often than they are in the literature. Many published studies use small sample sizes and ordinal or categorical outcomes. The statistical assumptions of more power parametric statistics can often not be met with these types of designs. Non-parametric statistics are robust to these violations and should be used accordingly. Post hoc analyses are important in non-parametric statistics, just like in parametric statistics.
In a prior post, I explained how Mann-Whitney U tests were used in a post hoc fashion for significant main effects found with Kruskal-Wallis analyses. This is pertinent for between-subjects tests.
If you are using a within-subjects design with three or more observations of a dichotomous categorical outcome, you utilize Cochran's Q test to assess main effects. If a significant main effect is found, then McNemar's tests have to be employed for post hoc group comparisons. Significant post hoc tests (or relative risk calculations) will provide evidence of significant differences across observations or within-subjects.
Non-parametric statistics should be employed more often than they are in the literature. Many published studies use small sample sizes and ordinal or categorical outcomes. The statistical assumptions of more power parametric statistics can often not be met with these types of designs. Non-parametric statistics are robust to these violations and should be used accordingly. Post hoc analyses are important in non-parametric statistics, just like in parametric statistics.
