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I have a data set which includes 5 binary variables per row of data. I was planning on creating a logistic regression to use 4 of the variables to predict the 5th and measure the significance (if any) of each variable.

Before I do that, I thought I'd look at more simple relationships between the 4 predictors.

What technique can I use to do this? A correlation matrix wouldn't be suitable as they are binary variables.

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    $\begingroup$ Take a look at Corex, "Correlation Explanation" github.com/gregversteeg/CorEx. The idea is to discover unseen/latent factors that generate your 4 (5?) variables. The number of variables is smallish but can give you insights which group together and with what weight. There might be some hierarchy in the data after all! $\endgroup$ – Vladislavs Dovgalecs Jun 7 '16 at 22:12
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    $\begingroup$ You can still estimate a correlation matrix. $\endgroup$ – Matthew Gunn Jun 7 '16 at 22:51
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Log-linear analysis is a well established, though in some fields largely forgotten, technique for investigating the relationship between multiple categorical variables.

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