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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 .
Statistical tests are used to test the fit between a hypothesis and the data. [1] [2] Choosing the right statistical test is not a trivial task. [1] The choice of the test depends on many properties of the research question. The vast majority of studies can be addressed by 30 of the 100 or so statistical tests in use. [3] [4] [5]
A rating scale is a set of categories designed to obtain information about a quantitative or a qualitative attribute. In the social sciences, particularly psychology, common examples are the Likert response scale and 0-10 rating scales, where a person selects the number that reflecting the perceived quality of a product.
An example is a preference ranking. Some data are measured at the interval level. Numbers indicate the magnitude of difference between items, but there is no absolute zero point. 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.
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 ...
Behaviorally anchored rating scales (BARS) are scales used to rate performance.BARS are normally presented vertically with scale points ranging from five to nine. It is an appraisal method that aims to combine the benefits of narratives, critical incidents, and quantified ratings by anchoring a quantified scale with specific narrative examples of good, moderate, and poor performance.
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The concept of data type is similar to the concept of level of measurement, but more specific. 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).