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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]
Level of measurement or scale of measure is a classification that describes the nature of information within the values assigned to variables. [1] Psychologist Stanley Smith Stevens developed the best-known classification with four levels, or scales, of measurement: nominal, ordinal, interval, and ratio.
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]
Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature.
This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables. General tests [ edit ]
Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature.
If for years 1 and 2 (possibly a span of 20 years apart), the nominal wage and price level P of goods are respectively nominal wage rate: $10 in year 1 and $16 in year 2 price level: 1.00 in year 1 and 1.333 in year 2, then real wages using year 1 as the base year are respectively: $10 (= $10/1.00) in year 1 and $12 (= $16/1.333) in year 2.
For a nominal variable a one-way chi-square (goodness of fit) test can help determine if our sample matches that of some population. [12] For interval and ratio level data, a one-sample t-test can let us infer whether the mean in our sample matches some proposed number (typically 0).