# Better Assumptions, Stronger Conclusions: The Case for Ordinal Regression in HCI

arXiv: https://arxiv.org/abs/2602.18660 · HTML: https://mlsanity.com/paper/2602.18660/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2026.

## Built on by (most cited first, 2 of 2)

- [CAFE: A Compound-AI Factorial Evaluation Framework](https://mlsanity.com/paper/2607.10380/) (2026)
- [How to Run Statistics over LLM Judges and Trust the Results: Calibrated Inference for Small-Sample AI Evaluation with evalstats](https://mlsanity.com/paper/2609.35815/) (2026)

## Abstract (arXiv)

Despite the widespread use of ordinal measures in HCI, such as Likert-items, there is little consensus among HCI researchers on the statistical methods used for analysing such data. Both parametric and non-parametric methods have been extensively used within the discipline, with limited reflection on their assumptions and appropriateness for such analyses. In this paper, we examine recent HCI works that report statistical analyses of ordinal measures. We highlight prevalent methods used, discuss their limitations and spotlight key assumptions and oversights that diminish the insights drawn from these methods. Finally, we champion and detail the use of cumulative link (mixed) models (CLM/CLMM) for analysing ordinal data. Further, we provide practical worked examples of applying CLM/CLMMs using R to published open-sourced datasets. This work contributes towards a better understanding of the statistical methods used to analyse ordinal data in HCI and helps to consolidate practices for future work.