1.4:
Nominal Level of Measurement
In statistics, the context of the data is critical to decide the right statistical method. For instance, taking the average of all the numbers to get a common phone number for the class is meaningless.
For the purpose of analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured or ordered but can be grouped into categories fall under the nominal level of measurement. For example, one cannot measure or place hairstyles in a particular order but can group them into different categories.
Although these groups can be numbered for the sake of convenience, the difference between these numbers or the average of these numbers is meaningless.
Survey responses such as like-dislike, yes-no, or political party affiliations also fall under the nominal level of measurement.
1.4:
Nominal Level of Measurement
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