{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-practical-guide-and-software-for-analysing","title":"A practical guide and software for analysing pairwise comparison experiments","arxiv_id":"1712.03686","date":"2017-12-11","proceeding":null,"authors":["Maria Perez-Ortiz","Rafal K. Mantiuk"],"abstract":"Most popular strategies to capture subjective judgments from humans involve\nthe construction of a unidimensional relative measurement scale, representing\norder preferences or judgments about a set of objects or conditions. This\ninformation is generally captured by means of direct scoring, either in the\nform of a Likert or cardinal scale, or by comparative judgments in pairs or\nsets. In this sense, the use of pairwise comparisons is becoming increasingly\npopular because of the simplicity of this experimental procedure. However, this\nstrategy requires non-trivial data analysis to aggregate the comparison ranks\ninto a quality scale and analyse the results, in order to take full advantage\nof the collected data. This paper explains the process of translating pairwise\ncomparison data into a measurement scale, discusses the benefits and\nlimitations of such scaling methods and introduces a publicly available\nsoftware in Matlab. We improve on existing scaling methods by introducing\noutlier analysis, providing methods for computing confidence intervals and\nstatistical testing and introducing a prior, which reduces estimation error\nwhen the number of observers is low. Most of our examples focus on image\nquality assessment.","url_abs":"http://arxiv.org/abs/1712.03686v2","url_pdf":"http://arxiv.org/pdf/1712.03686v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-practical-guide-and-software-for-analysing","repo_url":"https://github.com/mantiuk/pwcmp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-practical-guide-and-software-for-analysing","repo_url":"https://github.com/gfxdisp/pwcmp_rating_unified","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-quality-assessment","task_name":"Image Quality Assessment"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.03686","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}