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Ordinal data is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories are not known. [1]: 2 These data exist on an ordinal scale, one of four levels of measurement described by S. S. Stevens in 1946.
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.
Ordinal regression turns up often in the social sciences, for example in the modeling of human levels of preference (on a scale from, say, 1–5 for "very poor" through "excellent"), as well as in information retrieval. In machine learning, ordinal regression may also be called ranking learning. [3] [a]
Scaling of data: One of the properties of the tests is the scale of the data, which can be interval-based, ordinal or nominal. [3] Nominal scale is also known as categorical. [6] Interval scale is also known as numerical. [6] When categorical data has only two possibilities, it is called binary or dichotomous. [1]
Examples are attitude scales and opinion scales. Some data are measured at the ratio level. Numbers indicate magnitude of difference and there is a fixed zero point. Ratios can be calculated. Examples include: age, income, price, costs, sales revenue, sales volume, and market share.
The Mohs scale is an ordinal scale. For example, corundum (9) is twice as hard as topaz (8), but diamond (10) is four times as hard as corundum. [ citation needed ] The table below shows the comparison with the absolute hardness measured by a sclerometer , with images of the reference minerals in the rightmost column.
For example, count data requires a different distribution (e.g. a Poisson distribution or binomial distribution) than non-negative real-valued data require, but both fall under the same level of measurement (a ratio scale). Various attempts have been made to produce a taxonomy of levels of measurement.
For ordinal variables the median can be calculated as a measure of central tendency and the range (and variations of it) as a measure of dispersion. For interval level variables, the arithmetic mean (average) and standard deviation are added to the toolbox and, for ratio level variables, we add the geometric mean and harmonic mean as measures ...