Nominal category

[1] The first type of categorical scale is dependent on natural ordering, levels that are defined by a sense of quality.

Additionally, data identification justifies whether it is necessary to form new nominal groups based on the information available.

While arithmetic operations and calculations measuring the central tendency of data (quantitative assignments of data analysis, including mean, median) cannot be performed on nominal categories, performable data operations include the comparison of frequencies and the frequency distribution, the determination of a mode, the creation of pivot tables, and uses of Chi-square goodness of fit and independence tests, coding and recoding, and logistic or probit regressions.

For example, figuring out whether proportionally more Canadians have first names starting with the letter 'a' than non-Canadians would be a fairly arbitrary, random exercise.

However, the use of comparing nominal data with a frequency distribution to associate gender and political affiliation would be more effective since a correlation between the counts of a particular party affiliation would compare to the number of male and or female voters accounted in a dataset.

From a quantitative analysis perspective, one of the most common operations to perform on nominal data is dummy variable assignment, a method earlier introduced.