A Method to Maximize the Information of a Continuous Variable in Relation to a Dichotomous Grouping Variable: Cutpoint Analysis

E-mail: [email protected] In statistical analyses the researcher should normally use all the relevant information in the data. This argument has been used to advise against the habit of dichotomizing (approximately) continuous variables. However, if, for instance, a continuous variable is not normally distributed, it is possible that an optimal dichotomization can reveal relationships between variables otherwise obscured. Two analytical situations when this might apply were treated: 1. The study of the relationship between an independent dichotomous grouping variable and a dependent continuous variable and 2. the discrimination between two groups by identifying an optimal cutpoint in one or more continuous variables, treated as the predictor(s). For these purposes, cutpoint analysis (CPA) is introduced as a method for finding an optimal categorization of a continuous variable together with a computer package (ROPstat) to carry out the analysis. Three empirical examples are given that show the usefulness of CPA as compared to conventional analyses.