The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of candidate predictor variables. In order to demonstrate the process of conducting a logistic regression analysis, models are generated using educational background measures (e.g., last grade in English, high school grade point average, etc.) to predict the dichotomous outcome of success (i.e., grade of A, B, C, or CR) in an English course. At the same time, the information presented here can be applied to other logistic regression studies. Topics covered include setting up the database, dummy coding, data reduction, multicollinearity, missing cases, setting the cutoff value, interpretation of the results, selecting a model, and the interpretation of odds ratios when they are negative. (Contains 2 figures and 2 tables.)