IEEE VIS 2024 Content: FAVis: Visual Analytics of Factor Analysis for Psychological Research

FAVis: Visual Analytics of Factor Analysis for Psychological Research

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Yikai Lu - University of Notre Dame, Notre Dame, United States. University of Notre Dame, Notre Dame, United States

 Chaoli Wang - University of Notre Dame, Notre Dame, United States

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 Room: Bayshore VI

2024-10-17T16:00:00ZGMT-0600Change your timezone on the schedule page
2024-10-17T16:00:00Z

Exemplar figure, described by caption below — We propose FAVis (https://luyikei.github.io/favis/). (A) Matrix view shows a factor loadings matrix; (B) Network view visualizes cross-loadings most effectively; (C) Parallel-coordinates view shows factor loadings for each variable/factor allows for selecting variables/factors within a range; (D) Tag view shows the relevance of tags for each factor by counting tags annotated for variables based on a theory; (E) Word cloud view helps interpret factors by correlating fonts with the values of factor loadings; (F) Threshold view controls the number of factor loadings shown in different views; (G) Factor correlation view shows the network of factor correlations; (H) Top bar for filtering.

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Keywords

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

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.