A practical guide and software for analysing pairwise comparison experiments

TL;DR

This paper provides a practical guide and Matlab software for analyzing pairwise comparison experiments, improving data scaling methods with outlier detection, confidence intervals, and a prior to enhance measurement accuracy.

Contribution

It introduces a comprehensive software tool for pairwise comparison analysis, incorporating novel features like outlier detection, confidence interval computation, and a prior to improve estimation with limited data.

Findings

Enhanced scaling methods with outlier analysis

Methods for confidence intervals and statistical testing

Reduced estimation error with a new prior

Abstract

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.