Submitting Campus
Daytona Beach
Department
Human Factors and Behavioral Neurobiology
Document Type
Article
Publication/Presentation Date
4-28-2022
Abstract/Description
Comparing products, features, brands, or ideas relative to one another is a common goal in user experience (UX) and market research. While Likert-type scales and ordinal stack ranks are often employed as prioritization methods, they are subject to several psychometric shortcomings. We introduce the numeric forced rank, a lightweight approach that overcomes some of the limitations of standard methods and allows researchers to collect absolute ratings, relative preferences, and subjective comments using a single scale. The approach is optimal for UX and market research, but is also easily employed as a structured decision-making exercise outside of consumer research. We describe how the numeric forced rank was used to determine the name of a new Google Cloud Platform (GCP) feature, present the findings, and make recommendations for future research.
Publication Title
CHI EA '22: CHI Conference on Human Factors in Computing Systems Extended Abstracts
DOI
https://doi.org/10.1145/3491101.3503550
Publisher
Association for Computing Machinery
Sponsorship/Conference/Institution
CHI '22: CHI Conference on Human Factors in Computing Systems
Location
New Orleans, LA, USA
Number of Pages
4
Scholarly Commons Citation
Gannon, E., & Chaparro, B. (2022). Numeric Forced Rank: A Lightweight Method for Comparison and Decision-making. CHI EA '22: CHI Conference on Human Factors in Computing Systems Extended Abstracts, (). https://doi.org/10.1145/3491101.3503550
