Question: What is your goal?
To determine the appropriate statistical method for analyzing data with more than two categories, consider whether you are interested in comparing proportions across multiple categories or testing the association among them.
Comparing Proportions
If your goal is to compare the proportions among multiple categories, select this option to explore methods that help determine if there is a significant difference in proportions.
- Chi-Square Test for Independence
- Use the chi-square test to compare the observed frequencies of multiple categorical groups to their expected frequencies. This test helps determine if there is a statistically significant difference in proportions among the categories. It is an extension of the chi-square test used for two categories and is useful for analyzing data from surveys, experiments, and other scenarios where you want to compare proportions across multiple groups.
Association
If your goal is to test the association among multiple categories, select this option to explore methods that help determine if there is a significant relationship among the categories.
- Fisher Exact Test For Larger Tables
- Use Fisher’s exact test for small sample sizes to determine if there is a nonrandom association among multiple categorical variables. While Fisher’s exact test is commonly used for two categories, it can also be applied to more than two categories when sample sizes are small, making it a reliable alternative to the chi-square test in such cases. This test is particularly useful in clinical research, biology, and other fields where precise conclusions about associations are needed despite limited data.
Choose the option that best matches your analysis goal to proceed.
[ Statistic Selector > Categorical Data | Statistics Contents ]
Last Modified: 06/13/2024