The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal scale is qualitative (categorical). Categories, colors, names, labels, favorite foods, and ‘yes’ or ‘no’ responses are examples of nominal level data. For example, one can group restaurants based on whether they serve vegetarian, non-vegetarian, or vegan diets. But one cannot measure how much healthier the diet of each restaurant is or how much more vegetarian it is than other restaurants.
This text is adapted from Openstax, Introductory Statistics, Section 1.3 Frequency, Frequency Tables, and Levels of Measurement
