FAVis: Visual Analytics of Factor Analysis for Psychological Research

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Psychological research often involves understanding psychological constructs through conducting factor analysis on data collected by a questionnaire, which can comprise hundreds of questions. Without interactive systems for interpreting factor models, researchers are frequently exposed to subjectivity, potentially leading to misinterpretations or overlooked crucial information. This paper introduces FAVis, a novel interactive visualization tool designed to aid researchers in interpreting and evaluating factor analysis results. FAVis enhances the understanding of relationships between variables and factors by supporting multiple views for visualizing factor loadings and correlations, allowing users to analyze information from various perspectives. The primary feature of FAVis is to enable users to set optimal thresholds for factor loadings to balance clarity and information retention. FAVis also allows users to assign tags to variables, enhancing the understanding of factors by linking them to their associated psychological constructs. Our user study demonstrates the utility of FAVis in various tasks.

Original languageEnglish (US)
Title of host publicationProceedings - 2024 IEEE Visualization Conference - Short Papers, VIS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages51-55
Number of pages5
ISBN (Electronic)9798350354850
DOIs
StatePublished - 2024
Externally publishedYes
Event2024 IEEE Visualization and Visual Analytics Conference, VIS 2024 - St. Pete Beach, United States
Duration: Oct 13 2024Oct 18 2024

Publication series

NameProceedings - 2024 IEEE Visualization Conference - Short Papers, VIS 2024

Conference

Conference2024 IEEE Visualization and Visual Analytics Conference, VIS 2024
Country/TerritoryUnited States
CitySt. Pete Beach
Period10/13/2410/18/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Coordinated and Multiple Views
  • High-dimensional Data
  • Machine Learning
  • Modelling and Simulation Applications
  • Statistics

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