Solved – How to adapt ANOVA for binary data

I have four competing models which I use to predict a binary outcome variable (say, employment status after graduating, 1 = employed, 0 = not-employed) for n subjects. A natural metric of model performance is hit rate which is the percentage of correct predictions for each one of the models.

It seems to me that I cannot use ANOVA in this setting as the data violates the assumptions underlying ANOVA. Is there an equivalent procedure I could use instead of ANOVA in the above setting to test for the hypothesis that all four models are equally effective?

Contingency table (chi-square). Also Logistic Regression is your friend – use dummy variables.

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