Interpreting the diversity in subjective judgments

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Abstract

In a CHI paper from 10 years ago, entitled "Accounting for Diversity in Subjective Judgments", an interesting dichotomy was reported between, on the one side, the increased use of idiosyncratic constructs when judging the user experience of diverse products and, on the other hand, the statistical methods available to analyze such data. The paper more specifically proposed a method to extract diverse perspectives (called views) from experimental data. The current paper provides three improvements of this existing method by: 1) showing that a little-known approach for clustering attributes, called VARCLUS, can be applied and extended to provide a more optimal algorithm, 2) showing how the VARCLUS method can be applied to perform both within- and across-subject analysis, and 3) providing access to the VARCLUS method by incorporating it in ILLMO, a user-friendly and freely available program for interactive statistics.

Original languageEnglish
Title of host publicationCHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages11
ISBN (Electronic)978-1-4503-5970-2
DOIs
Publication statusPublished - 2 May 2019
Event2019 CHI Conference on Human Factors in Computing Systems, CHI 2019: ACM CHI Conference on Human Factors in Computing Systems - Scottish Event Campus, Glasgow, United Kingdom
Duration: 4 May 20199 May 2019
https://chi2019.acm.org/

Conference

Conference2019 CHI Conference on Human Factors in Computing Systems, CHI 2019
Abbreviated titleCHI EA 19
CountryUnited Kingdom
CityGlasgow
Period4/05/199/05/19
Internet address

Keywords

  • Clustering
  • Diversity
  • Quantitative methods
  • Repertory grid
  • User experience

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