# Best way to measure correlation between multiple (>2) binomial variables?

What is the best (most simple and robust) test statistic to measure the overall degree of association (inter-dependence, correlation or covariance?) between multiple binary variables?

I have been looking at multiple regression, but I think this is too complex as it is used to model the actual relationship for prediction, rather than to measure the degree of correlation.

So let's say we have k binary (binomial) variables, and a sample size of n observations per variable, where each variable occurs (positive case) at a given frequency/probability f.

How would we measure the degree of correlation between these variables, and how does the p-value of that metric depend on n, k and f?

• Why not just calculate the pairwise sample correlations directly? Commented Mar 24, 2016 at 14:17
• Because that wouldn't give a single, overall correlation metric, which is what I would like to do probability analysis on to test for significance. Commented Mar 24, 2016 at 14:19
• stats.stackexchange.com/questions/103801/… may help. Think geometrically: coding with 0 and 1 there are four points (0, 0), (0, 1), (1, 0) and (1, 1) to bivariate data. A correlation makes sense so long as both variables are not constant. Whether it is the best method for your purpose is a different question. Commented Mar 24, 2016 at 14:22
• Are you saying that you want a single measure summarizing all the bivariate relationships among several variables simultaneously? I can't see much meaning to that if so. Perhaps the road leads to some flavour of correspondence analysis. Commented Mar 24, 2016 at 14:45
• I think some people use principal component analysis (PCA) on binary variables. I think that divides the experts on whether it is sound. There may be some threads here. Commented Mar 24, 2016 at 15:00

First, whatever you use it won't be correlation. Correlation is about two variables.

Second, there is no simple way to do this because "degree of association" is not easily defined with multiple variables.

Third, as @NickCox commented yesterday, some people do principal components analysis on binary data but 1) This isn't simple 2) It's a bit controversial and 3) It may not give you what you want.

Fourth, have you considered log-linear analysis? This is a sort of generalization of chi-square: It makes no assumption about a dependent variable.

• Thanks, I will look into log-linear analysis. Do you have any references (links) on how it could be applied in this case? Otherwise I'll just have to feel and bump my way around as I've been doing... Commented Mar 26, 2016 at 12:58
• I don't have any particularly good links; Googling will find lots of stuff. Commented Mar 26, 2016 at 13:07

That would be Cramers V, a measure for dependencies between two nominal variables: https://en.m.wikipedia.org/wiki/Cram%C3%A9r%27s_V

The Pearson chi-squared test is the way to determine if the dependency is significant: https://en.m.wikipedia.org/wiki/Pearson%27s_chi-squared_test

• Thanks, Pieter, is there are way to calculate minimum sample size, given that some variables may occur with very low frequency (and so may not appear at all, if the sample is too small)?? Also, how does it work for multiple binomial variables? Commented Mar 24, 2016 at 14:39
• You mean a power computation? Or just the chance that a variable has at least one positive given the sample size and the probability of one item being positive? Commented Mar 24, 2016 at 14:43
• Yes, I mean a power calculation. Ideally, I am looking to understand how the minimum sample size for such a correlation test depends on the number of binomial variables k, their frequency f, and any other critical parameters (e.g., alpha, beta, R^2, etc.). Commented Mar 24, 2016 at 14:45
• After some research, I don't think Cramer's V can be used to measure correlation between multiple (k>2) binomial variables, can it? It seems to be geared only for 2 variables, in 2-dimensional contingency tables... Commented Mar 26, 2016 at 12:08
• Yes, that's correct. I did not notice that. It measures the dependency between 2 nominal variables. Commented Mar 26, 2016 at 12:11