Experiments in psychology or education often use logistic regression models (LRMs) when analyzing binary outcomes. However, a challenge with LRMs is that results are generally difficult to understand. We present alternatives to LRMs in the analysis of experiments and discuss the linear probability model, the log-binomial model, and the modified Poisson regression model. A Monte Carlo simulation assessed bias in point estimates and standard errors as well as power and Type I error rates of the different methods. Findings show that the linear probability and the modified Poisson regression models are valid, unbiased, and in some cases, better alternatives to the LRM when the predictor of interest is a binary variable. An applied example is provided as well.