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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 .
Data hierarchy refers to the systematic organization of data, often in a hierarchical form. Data organization involves characters, fields, records, files and so on. [1] [2] This concept is a starting point when trying to see what makes up data and whether data has a structure. For example, how does a person make sense of data such as 'employee ...
1 bit – 0 or 1, false or true, Low or High (a.k.a. unibit) 1.442695 bits (log 2 e) – approximate size of a nat (a unit of information based on natural logarithms) 1.5849625 bits (log 2 3) – approximate size of a trit (a base-3 digit) 2 1: 2 bits – a crumb (a.k.a. dibit) enough to uniquely identify one base pair of DNA
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.
A standard representation of the pyramid form of DIKW models, from 2007 and earlier. [1] [2]The DIKW pyramid, also known variously as the knowledge pyramid, knowledge hierarchy, information hierarchy, [1]: 163 DIKW hierarchy, wisdom hierarchy, data pyramid, and information pyramid, [citation needed] sometimes also stylized as a chain, [3]: 15 [4] refer to models of possible structural and ...
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 ).
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Multilevel models can be used on data with many levels, although 2-level models are the most common and the rest of this article deals only with these. The dependent variable must be examined at the lowest level of analysis. [1]