Introduction Logistic regression is often used to analyze experiments with binary outcomes (e.g., pass vs fail) and binary predictors (e.g., treatment vs control). Although appropriate, there are other possible models that can be run that may provide easier to interpret results.
In addition, some of these models may be quicker to run. Some may say that this point is moot given the availability of computing power today but if you’ve ever tried to run a hierarchical generalized linear model with a logit link function and a binary outcome, you know that when using R (using glmer or nlme) this may take quite a long time (and cross your fingers that you don’t have convergence issues).

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