Nominal category in the context of "Qualitative property"

Qualitative geography is a subfield and methodological approach to geography focusing on nominal data, descriptive information, and the subjective and interpretive aspects of how humans experience and perceive the world. Often, it is concerned with understanding the lived experiences of individuals and groups and the social, cultural, and political contexts in which those experiences occur. Thus, qualitative geography is traditionally placed under the branch of human geography; however, technical geographers are increasingly directing their methods toward interpreting, visualizing, and understanding qualitative datasets, and physical geographers employ nominal qualitative data as well as quanitative. Furthermore, there is increased interest in applying approaches and methods that are generally viewed as more qualitative in nature to physical geography, such as in critical physical geography. While qualitative geography is often viewed as the opposite of quantitative geography, the two sets of techniques are increasingly used to complement each other. Qualitative research can be employed in the scientific process to start the observation process, determine variables to include in research, validate results, and contextualize the results of quantitative research through mixed-methods approaches.

In statistics, a categorical variable (also called qualitative variable) is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property. In computer science and some branches of mathematics, categorical variables are referred to as enumerations or enumerated types. Commonly (though not in this article), each of the possible values of a categorical variable is referred to as a level. The probability distribution associated with a random categorical variable is called a categorical distribution.

Categorical data is the statistical data type consisting of categorical variables or of data that has been converted into that form, for example as grouped data. More specifically, categorical data may derive from observations made of qualitative data that are summarised as counts or cross tabulations, or from observations of quantitative data grouped within given intervals. Often, purely categorical data are summarised in the form of a contingency table. However, particularly when considering data analysis, it is common to use the term "categorical data" to apply to data sets that, while containing some categorical variables, may also contain non-categorical variables. Ordinal variables have a meaningful ordering, while nominal variables have no meaningful ordering.