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AÂ **Phi Coefficient** (sometimes called a *mean square contingency coefficient*) is a measure of the association between two binary variables.

For a given 2Ã—2 table for two random variablesÂ *xÂ *andÂ *y*:

The Phi Coefficient can be calculated as:

**Î¦ = (AD-BC) / âˆš(A+B)(C+D)(A+C)(B+D)**

**Example: Calculating a Phi Coefficient in R**

Suppose we want to know whether or not gender is associated with political party preference so we take a simple random sample of 25 voters and survey them on their political party preference.

The following table shows the results of the survey:

We can use the following code to enter this data into a 2Ã—2 matrix in R:

#create 2x2 table data = matrix(c(4, 8, 9, 4), nrow = 2) #view dataset data [,1] [,2] [1,] 4 9 [2,] 8 4

We can then use the phi() function from theÂ **psych** package to calculate the Phi Coefficient between the two variables:

#load psych package library(psych) #calculate Phi Coefficient phi(data) [1] -0.36

The Phi Coefficient turns out to beÂ **-0.36**.

Note that the phi function rounds to 2 digits by default, but you can specify the function to round to as many digits as youâ€™d like:

#calculate Phi Coefficient and round to 6 digits phi(data, digits = 6) [1] -0.358974

**How to Interpret a Phi Coefficient**

Similar to a Pearson Correlation Coefficient, a Phi Coefficient takes on values between -1 and 1 where:

**-1**indicates a perfectly negative relationship between the two variables.**0**indicates no association between the two variables.**1**indicates a perfectly positive relationship between the two variables.

In general, the further away a Phi Coefficient is from zero, the stronger the relationship between the two variables.

In other words, the further away a Phi Coefficient is from zero, the more evidence there is for some type of systematic pattern between the two variables.

**Additional Resources**

An Introduction to the Phi Coefficient

Phi Coefficient Calculator