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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 .
In statistics, scale analysis is a set of methods to analyze survey data, in which responses to questions are combined to measure a latent variable. These items can ...
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.
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.
The study of values has a long history in psychological research, for instance, Rokeach's value scale, which has been used widely. [1] That focus has also been transferred to the study of culture in psychology. The anthropologists Kluckhohn and Strodtbeck conducted a large-scale study of values in five Southwestern US cultures in the 1950s. [2]
By 1980, the values scale had fallen into disuse due to its archaic content, lack of religious inclusiveness, and dated language. Richard E. Kopelman, et al., recently updated the Allport-Vernon-Lindzey Study of Values. The motivation behind their update was to make the value scale more relevant to today; they believed that the writing was too ...
In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in a way that eliminates the effects of certain gross influences, as in an anomaly time series. Some ...
The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).