Coding of Categorical and Continuous Predictors
When developing a prediction model, an important consideration is how we code the predictors. Raw data from a study are often not in a form appropriate for entering in regression models and must first be manipulated. This is known as “coding.” As in any data analysis, we will usually start with obtaining an impression of the data under study, such as occurrence of missing values and the distribution of predictors. Descriptive analyses, such as frequency tables are useful to this aim. We will consider various issues in coding of unordered and ordered categorical predictors. For continuous predictors, we specifically discuss how we can limit the influence of outliers and interpret regression coefficients.