Metadata
- Author: Karen Grace Martin
- Full Title:: Five Ways to Analyze Ordinal Variables
- Category:: 🗞️Articles
- Document Tags:: Statistics,
- URL:: https://substack.com/redirect/38a2896d-fe3b-4d24-99ae-8422f30bfff7?j=eyJ1IjoiNDRpMmEifQ.txKr3BEB06jM7pp-5wphmyXof7jFdPvpfRX5kIjhK8g
- Finished date:: 2024-01-28
Highlights
- Analyze ordinal variables as if they’re nominal (View Highlight)
The biggest advantage to this approach is you won’t violate any assumptions. (View Highlight)
Analyze ordinal variables as if they’re numeric (View Highlight)
This approach requires the assumption that the distance between each set of subsequent categories is equal. And that can be very difficult to justify. (View Highlight)
- Non-parametric tests (View Highlight)
Many non-parametric descriptive statistics are based on ranking numerical values. Ranks are themselves ordinal — they tell you information about the order, but no distance between values (View Highlight)
Common rank-based non-parametric tests include Kruskal-Wallis, Spearman correlation, Wilcoxon-Mann-Whitney, and Friedman (View Highlight)
- Ordinal logistic & probit regression (View Highlight)
These models are complex, have their own assumptions, and can take some practice to interpret. But they are also sometimes exactly what you need. (View Highlight)
The basic idea is a rank transformation: transform each ordinal outcome score into the rank of that score and run your regression, two-way ANOVA, or other model on those ranks. The thing to remember though, is that all results need to be interpreted in terms of the ranks. Just as a log transformation on a dependent variable puts all the means and coefficients on a log(DV) scale, the rank transformation puts everything on a rank scale. Your interpretations are going to be about mean ranks, not means (View Highlight)