248-2009: Learning When to Be Discrete: …

SAS Global Forum 2009 Statistics and Data Analysis Paper 248 2009. Learning When to Be discrete : continuous vs. categorical predictors David J. Pasta, ICON Clinical Research, San Francisco, CA. ABSTRACT. Some predictors , such as age or height, are measured as continuous variables but could be put into categories ("discretized"). Other predictors , such as occupation or a Likert scale rating, are measured as (ordinal) categories but could be treated as continuous variables. This paper explores choosing between treating predictors as continuous or categorical (including them in the CLASS statement). Specific topics covered include deciding how many categories to use for a discretized variable (is 3 enough? Is 6 too many?); testing for deviations from linearity by having the same variable in the model both as a continuous and as a CLASS variable; and exploring the efficiency loss when treating unequally spaced categories as though they were equally spaced.

1 Paper 248–2009 Learning When to Be Discrete: Continuous vs. Categorical Predictors David J. Pasta, ICON Clinical Research, San Francisco, CA ABSTRACT

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  Discrete, Continuous, Continuous vs, Categorical predictors, Categorical, Predictors

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