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Cramer’s V is a statistical measure used to determine the strength of association between two categorical variables. It is a number between 0 and 1, with 0 indicating no association and 1 indicating a perfect association. This measure is commonly used in fields such as social sciences and market research to identify relationships between variables and make informed decisions. It takes into account the sample size and the number of categories within each variable to provide a more accurate assessment of the association. Cramer’s V is a useful tool for analyzing data and identifying possible patterns or correlations between categorical variables.

## What is Cramer’s V?

**Cramer’s V** is used to understandthe strength of the relationship between two variables. To use it, your variables of interest should be categorical with two or more unique values per category. See more below.

*Cramer’s V is also known as Cramer’s Phi.*

## Assumptions for Cramer’s V

Every statistical method has assumptions. Assumptions mean that your data must satisfy certain properties in order for statistical method results to be accurate.

The assumptions for Cramer’s V include:

- Categorical variables

Let’s dive into what that means.

**Categorical**

For this test, your two variables must be categorical. A categorical variable is a variable that describes a category that doesn’t relate naturally to a number. Examples of categorical variables are eye color, city of residence, type of dog, etc.

## When to use the Cramer’s V?

You should use the Cramer’s V in the following scenario:

- You want to know the
**relationship**between two variables - Your variables of interest are
**categorical** - You have
**two or more unique values per category**

Let’s clarify these to help you know when to use Cramer’s V

**Relationship**

You are looking for a statistical test to look at how two variables are related. Other types of analyses include testing for a difference between two variables or predicting one variable using another variable (prediction).

**Categorical**

A categorical variable is a variable that describes a category that doesn’t relate naturally to a number. Examples of categorical variables are eye color, city of residence, type of dog, etc..

*If your data are continuous, Pearson Correlation may be more appropriate. If one of your variables is continuous and the other is binary, you should use Point Biserial Correlation. *

**Two or More Distinct Values**

To perform Cramer’s V, there must be two or more unique values in each of your categorical variables. If there are only two unique values, then using Cramer’s V is the same as using the *Phi Coefficient*.

## Cramer’s V Example

**Variable 1**: Political Party

**Variable 2**: Favorite Musical Genre

In this example, we are interested in investigating the relationship between political party and favorite musical genre. To begin, we collect these data from a group of people.

Because both of these variables are categorical with two or more possible values per variable, we know that Cramer’s V is a suitable test.

The analysis will result in a Cramer’s V value and a p-value. Cramer’s V ranges from 0 to 1, where 0 indicates no relationship and 1 indicates perfect association

The p-value represents the chance of seeing our results if there was no actual relationship between our variables. A p-value less than or equal to 0.05 means that our result is statistically significant and we can trust that the difference is not due to chance alone.