The transformation of quantitative variables into categories is a common practice in both experimental and observational studies. The typical procedure is to create groups by splitting the original variable distributi...The transformation of quantitative variables into categories is a common practice in both experimental and observational studies. The typical procedure is to create groups by splitting the original variable distribution at some cut point on the scale of measurement (e.g. mean, median, mode). Allegedly, dichotomization improves causal inference by simplifying statistical analyses. In this article, we address some of the adverse consequences of recoding quantitative variables into categories. In particular, we provide evidence that categorization usually leads to inefficient and biased estimates. We believe that considerable progress in our understanding of data analysis can occur if scholars follow the recommendations presented in this article. The recodification of quantitative variables as categorical is a poor methodological strategy, and scientists must stay away from it.展开更多
文摘The transformation of quantitative variables into categories is a common practice in both experimental and observational studies. The typical procedure is to create groups by splitting the original variable distribution at some cut point on the scale of measurement (e.g. mean, median, mode). Allegedly, dichotomization improves causal inference by simplifying statistical analyses. In this article, we address some of the adverse consequences of recoding quantitative variables into categories. In particular, we provide evidence that categorization usually leads to inefficient and biased estimates. We believe that considerable progress in our understanding of data analysis can occur if scholars follow the recommendations presented in this article. The recodification of quantitative variables as categorical is a poor methodological strategy, and scientists must stay away from it.
