Ordinal data uses categorical values with a meaningful natural order, but without a numerical scale that guarantees equal spacing. Sizes such as small, medium, and large, or survey responses from strongly disagree to strongly agree, are typical examples. section 6.1
Table 6.1 An overview of the different types of data
|
Name
|
Categorical or continuous?
|
Properties
|
Example
|
|---|---|---|---|
|
Nominal
|
Categorical
|
Data has discrete values, and categories are not ordered.
|
Colors, political parties
|
|
Ordinal
|
Categorical
|
Data has discrete values, and categories have a natural order, but spacing is uneven between categories.
|
T-shirt sizes (S/M/L), survey responses (good versus bad)
|
|
Interval
|
Continuous
|
Values are spaced evenly, but zero is not an absence of the measurement.
|
Temperature
|
|
Ratio
|
Continuous
|
Values are spaced evenly, and zero means an absence of the measurement.
|
Height, weight
|
Use the data-type overview to distinguish ordinal categories from nominal data and from interval or ratio measurements. section 6.1
A practical analysis usually involves these decisions:
- Inspect the actual values rather than trusting storage types: an integer column with only a few discrete values may represent ordered categories. section 6.1
- Check missing responses and distinguish meaningful ordinal answers from entries such as “No answer given” before analyzing relationships. section 6.4.1 section 7.2.2
- Use rank-based measures when relating an ordinal variable to a continuous one; Spearman’s rank correlation and Kendall’s tau are practical starting points. section 7.2.2
Survey trust ratings
A Likert-scale question asking how favorable or trustworthy respondents find AI tools produces ordinal data. To compare trust with years of professional coding experience, remove missing or non-answer responses, rank the trust categories, and calculate Spearman’s correlation or Kendall’s tau. section 6.4.1 section 7.2.2
