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Nominal Variable

  • Labels observations with categories that have no inherent order or ranking.
  • Used to classify individuals or groups (e.g., music preference, gender) for descriptive summaries.
  • Categories may be mutually exclusive or overlapping; nominal variables are typically used for descriptive purposes and are not used for statistical modeling.

A nominal variable is a type of categorical variable that assigns categories or labels to observations, but does not have any inherent order or ranking.

Nominal variables provide labels to classify observations into categories. Because the categories have no meaningful order or scale, they cannot be ranked or compared in terms of greater/lesser. Nominal variables can have multiple categories and those categories may be mutually exclusive (an individual belongs to only one category) or overlapping (an individual can belong to multiple categories). They are commonly used to describe and summarize data rather than to support modeling that relies on ordered or numeric relationships.

A study that classifies individuals by preferred type of music might use the nominal variable “music preference,” with categories such as “rock,” “pop,” “jazz,” and so on. These categories are labels used to classify individuals and do not imply any inherent order (for example, “rock” is not inherently better or worse than “pop”).

“Gender” is another example often used to classify individuals as male or female. These categories do not have an inherent order or ranking and are used as labels without further numeric meaning.

  • Political party affiliation
  • Religion
  • Race
  • Commonly used in social and behavioral sciences to classify individuals or groups based on characteristics or traits.
  • Typically used for descriptive summaries such as frequency tables and cross-tabulations.
  • Nominal variables do not have an inherent order, so they cannot be meaningfully placed on a scale.
  • According to the source text, nominal variables cannot be used for statistical analysis or data modeling and are instead typically used for descriptive purposes.
  • Categories may be mutually exclusive or overlapping; this distinction affects how data are recorded and summarized.
  • Categorical variable
  • Frequency tables
  • Cross-tabulations