期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Why Quantitative Variables Should Not Be Recoded as Categorical 被引量:1
1
作者 Antonio Fernandes Caio Malaquias +2 位作者 Dalson Figueiredo Enivaldo da Rocha rodrigo lins 《Journal of Applied Mathematics and Physics》 2019年第7期1519-1530,共12页
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
关键词 DICHOTOMIZATION INEFFICIENCY BIAS
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部