Names and Numbers: Types of Variables
Figure 1099 – Figure 1-1: Types of variables
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An ordinal variable is a set of ordered categories. A common example in the medical literature is the subjective judgment of disease staging in cancer, using categories such as stage 1, 2, or 3. Although we can safely say that stage 2 is worse than stage 1 but better than stage 3, we don’t really know by how much.
The other kinds of variables consist of actual measurements of individuals, such as height, weight, blood pressure, or serum electrolytes. Statisticians distinguish between interval variables, in which the interval between measurements is meaningful (for example, 32° to 38°C), and ratio variables, in which the ratio of the numbers has some meaning. Having made this distinction, they then analyze them all the same anyway. The important distinction is that these variables are measured quantities, unlike nominal and ordinal variables, which are qualitative in nature.
So where does the classification lead us? The important distinction is between the nominal and ordinal variables on one hand and the interval and ratio variables on the other. It makes no sense to speak of the average value of a nominal or ordinal variable—the average sex of a sample of patients or, strictly speaking, the average disability expressed on an ordinal scale. However, it is sensible to speak of the average blood pressure or average height of a sample of patients. For nominal variables, all we can deal with is the number of patients in each category. Statistical methods applied to these two broad classes of data are different. For measured variables, it is generally assumed that the data follow a bell curve and that the statistics focus on the center and width of the curve. These are the so-called parametric statistics. By contrast, nominal and ordinal data consist of counts of people or things in different categories, and a different class of statistics, called nonparametric statistics (obviously!), is used in dealing with these data.5343
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| 5343. | Norman GR, Streiner DL. PDQ Statistics . 3rd ed. Hamilton, Ontario: BC Decker Inc.; 2003. |