# A practical guide and software for analysing pairwise comparison experiments

arXiv: https://arxiv.org/abs/1712.03686 · HTML: https://mlsanity.com/paper/1712.03686/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2017.

## Built on by (most cited first, 18 of 18)

- [Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views](https://mlsanity.com/paper/2303.15206/) (2023)
- [Training a Task-Specific Image Reconstruction Loss](https://mlsanity.com/paper/2103.14616/) (2021)
- [DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4](https://mlsanity.com/paper/2305.14702/) (2023)
- [An Image Quality Assessment Dataset for Portraits](https://mlsanity.com/paper/2304.05772/) (2023)
- [NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods](https://mlsanity.com/paper/2412.08029/) (2024)
- [Subjective Image Quality Assessment with Boosted Triplet Comparisons](https://mlsanity.com/paper/2108.00201/) (2021)
- [Frequency Domain Loss Function for Deep Exposure Correction of Dark Images](https://mlsanity.com/paper/2104.10856/) (2021)
- [Descartes: Generating Short Descriptions of Wikipedia Articles](https://mlsanity.com/paper/2205.10012/) (2022)
- [HDRSDR-VQA: A Subjective Video Quality Dataset for HDR and SDR Comparative Evaluation](https://mlsanity.com/paper/2505.21831/) (2025)
- [GitRanking: A Ranking of GitHub Topics for Software Classification using Active Sampling](https://mlsanity.com/paper/2205.09379/) (2022)
- [Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement](https://mlsanity.com/paper/2303.01388/) (2023)
- [Which cycling environment appears safer? Learning cycling safety perceptions from pairwise image comparisons](https://mlsanity.com/paper/2412.09835/) (2024)
- [Pairwise Comparisons Are All You Need](https://mlsanity.com/paper/2403.09746/) (2024)
- [ICME 2025 Generalizable HDR and SDR Video Quality Measurement Grand Challenge](https://mlsanity.com/paper/2506.22790/) (2025)
- [Scoring Cycling Environments Perceived Safety using Pairwise Image Comparisons](https://mlsanity.com/paper/2307.13397/) (2023)
- [Evaluating Perceptual Distance Models by Fitting Binomial Distributions to Two-Alternative Forced Choice Data](https://mlsanity.com/paper/2403.10390/) (2024)
- [Towards Standardized Light Field Quality Assessment: Hybrid Subjective Benchmarking and Objective Metric Evaluation](https://mlsanity.com/paper/2607.03494/) (2026)
- [Multidimensional Observer Model and Perceptual Dimensions of Human Image Quality Assessment](https://mlsanity.com/paper/2609.38487/) (2026)

## Abstract (arXiv)

Most popular strategies to capture subjective judgments from humans involve the construction of a unidimensional relative measurement scale, representing order preferences or judgments about a set of objects or conditions. This information is generally captured by means of direct scoring, either in the form of a Likert or cardinal scale, or by comparative judgments in pairs or sets. In this sense, the use of pairwise comparisons is becoming increasingly popular because of the simplicity of this experimental procedure. However, this strategy requires non-trivial data analysis to aggregate the comparison ranks into a quality scale and analyse the results, in order to take full advantage of the collected data. This paper explains the process of translating pairwise comparison data into a measurement scale, discusses the benefits and limitations of such scaling methods and introduces a publicly available software in Matlab. We improve on existing scaling methods by introducing outlier analysis, providing methods for computing confidence intervals and statistical testing and introducing a prior, which reduces estimation error when the number of observers is low. Most of our examples focus on image quality assessment.