Search results
Results from the WOW.Com Content Network
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]
Nominal variables do not have a natural order, which means that statistical analyses of these variables will always produce the same results, regardless of the order in which the data is presented. [1] [3] Even though ordinal variable statistical methods cannot be used for nominal groups, nominal group methods can be used for both types of ...
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).
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]
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: indexes - measures that ...
The item-total correlation approach is a way of identifying a group of questions whose responses can be combined into a single measure or scale. This is a simple approach that works by ensuring that, when considered across a whole population, responses to the questions in the group tend to vary together and, in particular, that responses to no individual question are poorly related to an ...