When you fill out a survey, your responses are often aggregated and subjected to statistical analysis. Nominal variables are often gathered in order to place people into groups. Thus, nominal variables are also called categorical variables.
According to researchers at University of California, Los Angeles, nominal variables contain two or more categories without a natural ordering of the categories. They essentially label data collected in a study.
University of Delaware professor Dr. John H. McDonald notes that an individual nominal variable is usually a name, not a number.
McDonald mentions a common nominal variable--gender (male or female). Other examples include political affiliation, hair color and beverage preference.
Nominal variables are often described in terms of percentages or proportions, writes McDonald. For instance, when you hear a statistic that 42 percent of respondents were male and 58 percent were female, the tally of the nominal variable "gender" is being reported.
It is common for researchers to convert measurement variables into a nominal variable for analytical purposes. McDonald uses an example of grouping people into a "low" and "high” cholesterol group based on their numerical cholesterol levels, which is a measurement variable. A cutoff point is established; everyone below that figure falls into the low group, and everyone above goes in the high group.