Ordinal data is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories are...

In computer programming, an ordinal data type is a data type with the property that its values can be counted. That is, the values can be put in a one-to-one...

best-known classification with four levels, or scales, of measurement: nominal, ordinal, interval, and ratio. This framework of distinguishing levels of measurement...

Look up ordinal in Wiktionary, the free dictionary. Ordinal may refer to: Ordinal data, a statistical data type consisting of numerical scores that exist...

In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e....

In linguistics, ordinal numerals or ordinal number words are words representing position or rank in a sequential order; the order may be of size, importance...

statistics, ranking is the data transformation in which numerical or ordinal values are replaced by their rank when the data are sorted. For example, the...

In economics, an ordinal utility function is a function representing the preferences of an agent on an ordinal scale. Ordinal utility theory claims that...

{\displaystyle d} , originally developed by Norman Cliff for use with ordinal data,[dubious – discuss] is a measure of how often the values in one distribution...

Categorical data can be either nominal or ordinal. Ordinal data has a ranked order for its values and can therefore be converted to numerical data through...

methods can be used for both types of categorical data sets; however, nominally categorizing ordinal data will remove order, limiting further dataset analysis...

measurements (that outcomes include "how much better or worse"), and returns ordinal data, using only the modeled outcomes: the conclusion of a minimax analysis...

Alternative Methods of Estimation for Confirmatory Factor Analysis With Ordinal Data". Psychological Methods. 9 (4): 466–491. doi:10.1037/1082-989x.9.4.466...

data is distinct from that of binary data, in which the observations can take only two values, usually represented by 0 and 1, and from ordinal data,...

respects, to the extent to which Likert items are interpreted as being ordinal data. There are two primary considerations in this discussion. First, Likert...

measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful...

proportional odds logistic regression is an ordinal regression model—that is, a regression model for ordinal dependent variables—first considered by Peter...

("second") and B gets ranking number 3 ("third"). In computer data processing, ordinal ranking is also referred to as "row numbering". This method corresponds...

multinomial probit regression for categorical data. Ordered logit and ordered probit regression for ordinal data. Single index models[clarification needed]...

many MCDM algorithms to model and solve fuzzy problems. Ordinal data based methods Ordinal data has a wide application in real-world situations. In this...

like "this is the third largest city in the country", which are called ordinal numbers. Natural numbers are also used as labels, like jersey numbers on...

inversions of means. The problems were addressed by treating ordinal data with ordinal models, in particular an ordered-probit model. Frequentist techniques...

the lower half of the data set. The median and the mode are the only measures of central tendency that can be used for ordinal data, in which values are...

to carry out these procedures for continuous, ordinal, and heterogenous (continuous and ordinal) data. With the exception of Revelle and Rocklin's (1979)...

applied to ordinal data (ranked data): the MiniTab online documentation gives an example. However, this document notes: "When you have ordinal ratings,...

Quantiles can also be used in cases where only ordinal data are available. Values that divide sorted data into equal subsets other than four have different...

transformations. This may be important when there is outliers or when dealing with ordinal data. Wilcoxon signed-rank test Kruskal–Wallis one-way analysis of variance...

In terms of levels of measurement, non-parametric methods result in ordinal data. As non-parametric methods make fewer assumptions, their applicability...

robust against certain kinds of changes to a data set, and that (unlike the mean) it is meaningful for ordinal data. The concepts of an invariant estimator...

the main data analyses can and should be made: In the case of non-normals: should one transform variables; make variables categorical (ordinal/dichotomous);...