Death to Dichotomizing: Figure 1
这篇社论指出，将连续自变量二分（如中位数分割）会降低统计功效并可能产生虚假显著结果，呼吁消费者研究者避免这种做法，并提供了正确分析连续变量的建议。
Why call for a death to dichotomizing? The Journal of Consumer Research receives manuscripts on an almost daily basis in which researchers have dichotomized (often referred to as median splitting) a continuous independent variable. There are two principal problems with this approach to data analysis, each of which is very well documented in the literature. The first is that by dichotomizing continuous independent variables researchers are quite likely reducing the statistical power available to test their hypotheses (Irwin and McClelland 2003). The second, potentially more troubling, problem is that inappropriate dichotomizing of continuous data can at times create spurious significant results if the independent variables are correlated (Maxwell and Delaney 1993). And yet, despite these well-known problems, dichotomizing is an extremely frequent activity for experimental consumer researchers. The goal of this editorial is not to write an in-depth methodological piece on this subject but rather to briefly outline why all consumer researchers should be concerned about this topic. I also hope to illustrate how we can easily write up appropriately conducted analyses when our research designs include continuous independent variables. (For more thorough, methodological pieces on this topic I suggest an excellent and concise article in the Journal of Marketing Research [Irwin and McClelland 2001] or a truly comprehensive guide to performing analysis including continuous independent variables and interactions [Aiken and West 1991].) Likely the most common research design utilized by experimental consumer researchers is a very straightforward manipulation of one or more independent variables that the researcher believes will affect a dependent variable. Virtually all consumer researchers learn at one point how to describe and analyze such designs and are typically quite adept at it. For example, a researcher manipulates two independent variables between subjects and performs a two-by-two ANOVA examining their impact on a dependent variable …