ordinalbased

Ordinalbased is a term used to describe approaches and methods that rely primarily on ordinal information—the order of items—rather than interval or ratio measurements. In ordinalbased methods, the emphasis is on ranking and ordering rather than on the precise differences between values.

The term is used across disciplines such as statistics, psychology, and data science, where data are commonly collected on ordinal scales like Likert items, preference rankings, or class labels. Ordinalbased approaches seek to preserve and exploit the order information without assuming equal intervals between categories.

Common techniques within an ordinalbased framework include ordinal regression (also called cumulative link models), rank-based statistics (Spearman correlation, Kendall tau), and nonparametric tests (Friedman test). Data preprocessing may employ ordinal encoding techniques that reflect order, rather than treating categories as nominal labels or as numeric scores with assumed distances.

Advantages include robustness to outliers, minimal assumptions about the scale, and interpretability of order relations. Limitations involve loss of magnitude information, potential sensitivity to category definitions, and challenges in modeling interactions when only ordinal information is available.

Ordinalbased concepts overlap with and are distinct from ordinal scales, ranking, and ordinal regression. It is not a single formal framework but a descriptive label used to characterize methods that prioritize order information. See also ordinal scale, ranking, and ordinal regression.