Multiple correspondence analysis

Multiple correspondence analysis is a form of data analysis that looks at the relationships between multiple categories, or variables, at once. Imagine the categories are in the shape of a table, with each row representing a different category. Multiple correspondence analysis looks at how each category relates to the other categories by looking for patterns in the data. For example, if one of the categories is favorite color and another category is favorite food, we can use multiple correspondence analysis to figure out any patterns between the two categories (do people who like red more often like pizza?). This can tell us useful information about how different variables relate to each other and can help us better understand our data.