A practical guide and software for analysing pairwise comparison experiments
This work provides a practical tool for researchers in fields like image quality assessment, offering incremental improvements to scaling methods for pairwise comparison experiments.
The paper tackles the challenge of analyzing pairwise comparison data to create a measurement scale, introducing a Matlab software that improves on existing methods by adding outlier analysis, confidence intervals, and a prior to reduce estimation error with low observer counts, primarily applied to image quality assessment.
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.