Data that are labels rather than numbers. The label may describe a classification, category, or group of the item of interest. For example, for data on reasons people were absent from work, the classifications might include categories such as illness, vacation, holiday, or funeral leave. (See also Continuous data.)
data that arises by observing the group or class to which an outcome or an object belongs; often recorded as labels which may be alphabetical or numerical (e.g. gender observed as male or female may be recorded as M or F, and also as 0 or 1).
Data evaluated by sorting values into various categories(for example, severe, moderate, and mild). causality assessment Determining whether there is a reasonable possibility that the drug caused or contributed to an adverse event. It includes assessing temporal relationships, dechallenge/rechallenge information, association (or lack of association) with underlying disease, and the presence (or absence) of a more likely cause.
Uses numbers or labels with no implied numeric value, e.g. cause of death: cancer, cardiovascular, respiratory, other. This is unlike ordinal data which has an order or hierarchy. See also Data types.
Data at non-measurement level, grouped into categories. For example, nominal – gender, or ordinal – income band
Data in which the variables can only have discrete values.
Data that can be classified by type. The data is typically represented using a bar graph, circle graph, or pictograph. An example might be colors or breed of cat.
Categorical data fits into a small number of discrete categories (as opposed to continuous). Categorical data is either non-ordered (nominal) such as gender or city, or ordered (ordinal) such as high, medium, or low temperatures.