Students without prior research experience may not know how to conceptualize and design a study. This is the second of a two-part article that explains how an understanding of the classification and operationalization of variables is the key to the process. Variables need to be operationalized; that is, defined in a way that permits their accurate measurement. They may be operationalized as categorical or continuous variables. Categorical variables are expressed as category frequencies in the sample as a whole, while continuous variables are expressed as absolute numbers for each subject in the sample. Continuous variables should not be converted into categorical variables; there are many reasons for this, the most important being that precision and statistical power are lost. However, in certain circumstances, such as when variables cannot be accurately measured, when there is an administrative or public health need, or when the data are not normally distributed, it may be justifiable to do so. Confounding variables are those that increase (or decrease) the apparent effect of an independent variable on the dependent variable, thereby producing spurious (or suppressing true) relationships. These and other concepts are explained with the help of clinically relevant examples.

没有研究经验的学生可能不知道如何概念化和设计研究。这是由两部分组成的文章的第二部分，解释了对变量的分类和操作化的理解如何成为该过程的关键。变量需要可操作化；也就是说，以允许精确测量的方式定义。它们可以被操作为分类变量或连续变量。分类变量表示为整个样本中的类别频率，而连续变量表示为样本中每个受试者的绝对数。连续变量不应转换为分类变量；造成这种情况的原因有很多，最重要的是精度和统计功效的丧失。然而，在某些情况下，例如当变量无法准确测量时，当存在行政或公共卫生需要时，或者当数据不呈正态分布时，这样做可能是合理的。混杂变量是那些增加（或减少）自变量对因变量的明显影响，从而产生虚假（或抑制真实）关系的变量。这些和其他概念将借助临床相关示例进行解释。