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
Joint experiments(JEs)on small tokamaks have been regularly performed between 2005 and 2015 under the framework of the International Atomic Energy Agency(IAEA)coordinated research projects(CRPs).This paper describes t...Joint experiments(JEs)on small tokamaks have been regularly performed between 2005 and 2015 under the framework of the International Atomic Energy Agency(IAEA)coordinated research projects(CRPs).This paper describes the background and the rationale for these experiments,how they were organized and executed,main areas of research covered during these experiments,main results,contributions to mainstream fusion research,and discusses lessons learned and outcomes from these activities.We underline several of the most important scientific outputs and also specific outputs in the education of young scientists and scientists from developing countries and their importance.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.
基金supported by funding by the IAEA technical contracts within IAEA Coordinated Research Projects on‘Joint Research Using Small Tokamaks’and on‘Utilisation of a Network of Small Magnetic Confinement Fusion Devices for Mainstream Fusion Research’funded by Russian Science Foundation,Project 19-12-00312+3 种基金partly supported by the Competitiveness Program of NRNU MEPhIthe partial financial support from MEPhI and NRU MPEI in the framework of the Russian Academic Excellence Projectsupported by Tokamak Energy LtdOxford Instruments(UK)。
文摘Joint experiments(JEs)on small tokamaks have been regularly performed between 2005 and 2015 under the framework of the International Atomic Energy Agency(IAEA)coordinated research projects(CRPs).This paper describes the background and the rationale for these experiments,how they were organized and executed,main areas of research covered during these experiments,main results,contributions to mainstream fusion research,and discusses lessons learned and outcomes from these activities.We underline several of the most important scientific outputs and also specific outputs in the education of young scientists and scientists from developing countries and their importance.
