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.

没有先前研究经验的学生可能不知道如何概念化和设计研究。这是一篇由两部分组成的文章中的第二部分，该文章解释了对变量分类和操作化的理解如何是该过程的关键。变量需要操作化；也就是说，以允许其准确测量的方式定义。它们可以作为分类变量或连续变量进行操作。类别变量表示为样本整体中的类别频率，而连续变量表示为样本中每个主题的绝对数。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，最重要的是失去了精度和统计能力。然而，在某些情况下，例如，当变量无法准确测量时，当有行政或公共卫生需要时，或者当数据不是正态分布时，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。在临床相关示例的帮助下解释了这些和其他概念。