# McNemar's can be used as a post hoc test

## Significant main effects for Cochran's Q need to be explained

__like chi-square, fisher's exact test, Kruskal-Wallis, Cochran's Q, and Friedman's ANOVA__

**Non-parametric tests****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

**were used in a post hoc fashion for significant main effects found with**

__Mann-Whitney U__tests**analyses. This is pertinent for between-subjects tests.**

__Kruskal-Wallis__If you are using a

**within-subjects design**with

**three or more observations**of a

**dichotomous categorical outcome**, you utilize

__test to assess main effects. If a significant main effect is found, then__

**Cochran's Q**__tests have to be employed for__

**McNemar's****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

__of more power parametric statistics__

**statistical assumptions****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.