Dr. Gyeongcheol Cho
College of Arts and Sciences
The Ohio State University
Title: Exploring Structured Factor Analysis (SFA): From Theory to Practice
Abstract: Jöreskog's Covariance Structure Analysis (CSA; 1978), along with its maximum likelihood (ML) estimator, has long stood as the standard approach to structural equation modeling (SEM). Despite its wide adoption, it suffers from two persistent limitations: the occurrence of improper solutions (e.g., negative variance estimates) and the lack of an internal tool for making probabilistic inferences on true factor scores (e.g., estimating the probability that two individuals have different true factor scores). Addressing these challenges, Cho & Hwang (2023) proposed Structured Factor Analysis (SFA), a novel data matrix-based approach to SEM. SFA concurrently estimates model parameters and factor score distribution from a given data matrix, which prevents improper solutions and facilitates the probabilistic inference of true factor scores.
This workshop is designed to offer a succinct review of SFA theories and to grant participants the opportunity for hands-on experience with SFA through its associated software, SFA Prime. Attendees are encouraged to bring their personal laptops with the software pre-installed. The SFA Prime software is freely accessible on Windows 10/11 and Mac OS 11.6/12 platforms. Additionally, installing R is recommended for individuals interested in conducting further analyses.
Gyeongcheol Cho’s research interests are in advancing quantitative methods to explore and confirm the intricate hypothetical relationships that intertwine human behavior, psychology, and biological variables, including theoretical constructs. In the technical realm, his studies primarily encompass factor/component analyses, structural equation modeling, and interpretable machine learning.