Qualitative data

Not to be confused with Qualitative research.

The term qualitative   is used to describe certain types of information. The term is distinguished from the term quantitative data, in which items are described in terms of quantity and in which a range numerical values are used without implying that a particular numerical value refers to a particular distinct category. However, data originally obtained as qualitative information about individual items may give rise to quantitative data if they are summarised by means of counts; and conversely, data that are originally quantitative are sometimes grouped into categories to become qualitative data (for example, income below $20,000, income between $20,000 and $80,000, and income above $80,000).

Qualitative data describe items in terms of some quality or categorization that in some cases may be 'informal' or may use relatively ill-defined characteristics such as warmth and flavor; such subjective data are sometimes of less value to scientific research than quantitative data. However, qualitative data can include well-defined concepts such as gender, nationality or commodity type. Qualitative data can be binary (pass-fail, yes-no, etc.) or categorical data.

In regression analysis, dummy variables are a type of qualitative data. For example, if various features are observed about each of various human subjects, one such feature might be gender, in which case a dummy variable can be constructed that equals 0 if the subject is male and equals 1 if the subject is female. Then this dummy variable can be used as an independent variable (explanatory variable) in an ordinary least squares regression. Dummy variables can also be used as dependent variables, in which case the probit or logistic regression technique would typically be used.

See also

• Level of measurement
• Contingency table