In statistics, a nominal category (also nominal variable or nominal group) is a collection of objects or ideas grouped according to a particular qualitative property. Nominal categories do not have a natural order, which means that statistical analyses of these variables will always produce the same results, regardless of the order in which the data is presented. The first type of categorical scale is dependent on natural ordering, levels that are defined by a sense of quality. Variables with this ordering convention are known as ordinal variables. In comparison, variables with unordered scales are nominal variables.]]
Even though ordinal variable statistical methods cannot be used for nominal groups, nominal group methods can be used for both types of categorical data sets; however, nominally categorizing ordinal data will remove order, limiting further dataset analysis to result in nominal outcomes. For example, the effect of race (nominal) on income (ratio) could be investigated by regressing the level of income upon one or more dummy variables that specify race. When nominal variables are used in these contexts, the valid data operations that may be performed are limited. 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.]]
Examples and logical analysis of nominal data
As ‘nominal’ suggests, nominal groups are based on the name of the data it encapsulates. Another example of this is the use of indicator variable coding that assigns a numerical value of 0 or 1 to each data point in a set. This method identifies whether individual observations belong to a particular group (set to one) or not (set to zero).