Logistic Regression Analysis and Reporting: A Primer

Logistic regression, being well suited for analyzing dichotomous outcomes, has been increasingly applied in social science research. That potential expanded usage demands that researchers, editors, and readers be coached in terms of what to expect in an article that used the logistic regression technique: What tables should be included? What assumptions tested? What figures or charts should be expected? In this article we seek to answer these questions with an illustration of logistic regression applied to a real world data set. Results were evaluated and diagnosed in terms of the overall test of the model, interpretability and statistical significance of each predictor, goodness-of-fit statistics, predictive power, accuracy of prediction, and identification of potential outliers. Guidelines are offered for modeling strategies and reporting standards in logistic regression. Furthermore, 6 statistical packages were employed to perform logistic regression. Their strengths and weaknesses were noted in terms of flexibility, accuracy, completeness, and usefulness.