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).
A few years ago, I published an article on using Poisson, negative binomial, and zero inflated models in analyzing count data (see Pick Your Poisson). The abstract of the article indicates:
School violence research is often concerned with infrequently occurring events such as counts of the number of bullying incidents or fights a student may experience. Analyzing count data using ordinary least squares regression may produce improbable predicted values, and as a result of regression assumption violations, result in higher Type I errors.