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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.
[1]: 2 These data exist on an ordinal scale, one of four levels of measurement described by S. S. Stevens in 1946. The ordinal scale is distinguished from the nominal scale by having a ranking. [2] It also differs from the interval scale and ratio scale by not having category widths that represent equal increments of the underlying attribute. [3]
A variable used to associate each data point in a set of observations, or in a particular instance, to a certain qualitative category is a categorical variable. Categorical variables have two types of scales, ordinal and nominal. [1] The first type of categorical scale is dependent on natural ordering, levels that are defined by a sense of quality.
Scales constructed should be representative of the construct that it intends to measure. [6] It is possible that something similar to the scale a person intends to create will already exist, so including those scale(s) and possible dependent variables in one's survey may increase validity of one's scale.
These extensions converge with the family of intra-class correlations (ICCs), so there is a conceptually related way of estimating reliability for each level of measurement from nominal (kappa) to ordinal (ordinal kappa or ICC—stretching assumptions) to interval (ICC, or ordinal kappa—treating the interval scale as ordinal), and ratio (ICCs).
The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful rank order among values, and permit any one-to-one (injective) transformation. Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values, and ...
Composite measure in statistics and research design refer to composite measures of variables, i.e. measurements based on multiple data items. [1] An example of a composite measure is an IQ test, which gives a single score based on a series of responses to various questions. Three common composite measures include:
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]