What test could we use to compare percentages across three categories?

What test could we use to compare percentages across three categories?

If you are using categorical data you can use the Kruskal-Wallis test (the non-parametric equivalent of the one-way ANOVA) to determine group differences. If the test shows there are differences between the 3 groups. You can use the Mann-Whitney test to do pairwise comparisons as a post hoc or follow up analysis.

Can you use chi square for three groups?

The Chi square test for independence is appropriate if your interest is to test for association / relationship between two nominal (categorical) variables, which is not the case for your scenario, where you have only one categorical variable (prevalence) and you are interested to compare three groups on prevalence.

Is there a test to compare more than 2 proportions?

75+ 1 7 3 0 0 0 0 0 0 0… To compare k ( > 2) proportions there is a test based on the normal approximation. It consists of the calculation of a weighted sum of squared deviations between the observed proportions in each group and the overall proportion for all groups.

How do you compare k ( > 2 ) proportions?

To compare k ( > 2) proportions there is a test based on the normal approximation. It consists of the calculation of a weighted sum of squared deviations between the observed proportions in each group and the overall proportion for all groups. The test statistic has an approximate c 2 distribution with k −1 degrees of freedom.

How to compare more than 2 proportions without continuity correction?

To total the numbers of cases, and total numbers of observations for each age group, we use the tapply command: After this it is easy to perform the test: 6-sample test for equality of proportions without continuity correction H 0: The proportion of cases is the same in each age group: p 1 = p 2 = p 3 = p 4 = p 5 = p 6

How to compare prevalence of an outcome between 3 groups?

First, when you perform Chi-square test for the three groups together, you will get general idea about the differences between groups. Then you can perform the sub-effect test between only the group that had significantly higher prevalence with the other groups.