FAVis: Visual Analytics of Factor Analysis for Psychological Research
Yikai Lu, Chaoli Wang
TL;DR
FAVis tackles subjectivity in interpreting factor analysis for psychological research by delivering an interactive visual analytics tool that presents multiple views of factor loadings and correlations. It enables thresholding of factor loadings to balance clarity with information retention and allows tagging of variables to connect them with psychological constructs. A user study demonstrates FAVis’s utility across interpretation tasks, highlighting improvements in understanding latent constructs and their relationships. The work offers a practical approach to making factor analysis results more transparent and actionable for researchers working with questionnaire 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.