Ordinal data have categorical values (or levels) that fall naturally into a logical sequence, like the

severity of cancer (Stages I, II, III, and IV), or an agreement scale (often called a Likert scale)

with levels of strongly disagree, somewhat disagree, neither agree nor disagree, somewhat agree,

or strongly agree. Note that the levels are not necessarily equally spaced with respect to the

conceptual difference between levels.

Interval data represents numerical measurements where, unlike with ordinal classifications, the

difference (or interval) between two numbers is a meaningful measure in terms of being equally

spaced, but the zero point is completely arbitrary and does not denote the complete absence of

what you’re measuring. For example, a change from 20 to 25 degrees Celsius represents the same

amount of temperature increase as a change from 120 to 125 degrees Celsius. But 0 degrees

Celsius is purely arbitrary — it does not represent the total absence of temperature; it’s simply the

temperature at which water freezes (or, if you prefer, ice melts).

Ratio data, unlike interval data, does have a true zero point. The numerical value of a ratio

variable is directly proportional to how much there is of what you’re measuring, and a value of

zero means there’s nothing at all. Income and systolic blood pressure are good examples of ratio

data; an individual without a job may have zero income, which is not as bad as having a systolic

blood pressure of 0 mmHg, because then that individual would no longer be alive!

Statisticians may pontificate about levels of measurement excessively, pointing out cases that

don’t fall neatly into one of the four levels and bringing up various counterexamples.

Nevertheless, you need to be aware of the concepts and terminology in the preceding list because

you’ll see them in statistics textbooks and articles, and because teachers love to include them on

tests. The level of measurement of variables impacts how and to what precision data are

collected. Other level-of-measurement considerations include minimizing the data collected to

only what is needed, which also reduces data-privacy concerns and cost. And, more practically,

knowing the level of measurement of a variable can help you choose the most appropriate way to

analyze that variable.

Classifying and Recording Different Kinds of

Data

Although you should be aware of the four levels of measurement described in the preceding section,

you also need to be able to classify and deal with data in a more pragmatic way. The following

sections describe various common types of data you’re likely to encounter in the course of clinical and

other research. We point out some considerations you need to think through before you start collecting

your data.

Making bad decisions (or avoiding making decisions) about exactly how to represent the data

values in your research database can mess it up, and quite possibly doom the entire study to