Year: 2024
Type: conference-paper
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.
Authors: Yikai Lu, Chaoli Wang
Institution: University of Notre Dame
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: III: Small: Neural Field Representations for Scientific Visualization, III: Small: DeepRep: Unsupervised Deep Representation Learning for Scientific Data Analysis and Visualization, IMPLICIT CONTINUOUS REPRESENTATIONS FOR VISUALIZATION OF COMPLEX DATA, III: Medium: Collaborative Research: Deep Learning for In Situ Analysis and Visualization, OAC Core: A Machine Learning Assisted Visual Analytics Approach for Understanding Flow Surfaces
OpenAlex ID: W4404915761