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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. [1]
The data that all share a qualitative property form a nominal category. A variable which codes for the presence or absence of such a property is called a binary categorical variable, or equivalently a dummy variable.
In comparison, variables with unordered scales are nominal variables. [1] Visual difference between nominal and ordinal data (w/examples), the two scales of categorical data [2] A nominal variable, or nominal group, is a group of objects or ideas collectively grouped by a particular qualitative characteristic. [3]
Contemporary qualitative research has been influenced by a number of branches of philosophy, for example, positivism, postpositivism, critical theory, and constructivism. [7] The historical transitions or 'moments' in qualitative research, together with the notion of 'paradigms' (Denzin & Lincoln, 2005), have received widespread popularity over ...
is distributed as a chi-squared variable with n − 1 degrees of freedom. An alternative significance test for this index has been developed for large samples. [64] = / where m is the overall sample mean, n is the number of sample units and z is the normal distribution abscissa.
It is primarily used to look up specific values. In the example above, the table might have categorical column labels representing the name (a qualitative variable) and age (a quantitative variable), with each row of data representing one person (the sampled experimental unit or category subdivision).
Ordinal data analysis requires a different set of analyses than other qualitative variables. These methods incorporate the natural ordering of the variables in order to avoid loss of power. [ 1 ] : 88 Computing the mean of a sample of ordinal data is discouraged; other measures of central tendency, including the median or mode, are generally ...
As this is a logical (deterministic) and not a statistical (probabilistic) technique, with "crisp-set" QCA , the original application of QCA, variables can only have two values, which is problematic as the researcher has to determine the values of each variable. For example: GDP per capita has to be divided by the researcher in two categories ...