Contents
How do you find the correlation of a categorical variable in R?
Checking if two categorical variables are independent can be done with Chi-Squared test of independence. This is a typical Chi-Square test: if we assume that two variables are independent, then the values of the contingency table for these variables should be distributed uniformly.
For a Pearson correlation, each variable should be continuous. If one or both of the variables are ordinal in measurement, then a Spearman correlation could be conducted instead.
How to calculate correlation between two categorical variables?
Finally, with the rise of categorical variables in datasets, it is important to calculate correlations between this pair of variables (i.e., a categorical and another categorical variable). Let us start with a discussion surrounding computing correlation between two categorical variables.
How are correlation measures used in statistical analysis?
Due to their heavy historic use in statistical analyses, a family of tests have been developed to determine the significance of the difference between two categories of a variable compared to another categorical variable. A popular approach for dichotomous variables (i.e. variables with only two categories) is built on the chi-squared distribution.
How is the correlation ratio of a category defined?
Mathematically, it is defined as the weighted variance of the mean of each category divided by the variance of all samples; in human language, the Correlation Ratio answers the following question: Given a continuous number, how well can you know to which category it belongs to?
How are categorical variables converted into contingency tables?
When comparing two categorical variables, by counting the frequencies of the categories we can easily convert the original vectors into contingency tables. For example, imagine you wanted to see if there is a correlation between being a man and getting a science grant (unfortunately, there is a correlation but that’s a matter for another day).