“数字+”与统计数据工程系列讲座（六十七）6月25日密歇根大学徐功军教授来中心讲座预告

讲座题目：Covariate-Adjusted Generalized Factor Analysis with Application to Testing Fairness

主讲人：徐功军

讲座时间：2024年6月25日11:00-12:30

讲座地点：

线下：综合楼644    

线上：腾讯会议 215-672-249

主讲人简介：

Dr. Gongjun Xu is an Associate Professor in the Department of Statistics with a joint appointment in the Department of Psychology at the University of Michigan. He received his Ph.D. in Statistics from Columbia University in 2013. His research interests include latent variable models, psychometrics, statistical learning and inference, and survival analysis. Dr. Xu received NSF CAREER Award (2019), International Chinese Statistical Association (ICSA) Outstanding Young Researcher Award (2019), Bernoulli Society New Researcher Award (2019), Psychometric Society Early Career Award (2023) and Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award (2023). Dr. Xu is currently serving as Co-Editor-in-Chief for the Journal of Educational and Behavioral Statistics, and Associate Editor for Journal of American Statistical Association, Psychometrika, Annals of Applied Statistics, Statistica Sinica, and Journal of Data Science.

讲座摘要：    

In the era of data explosion, psychometricians and statisticians have been developing interpretable and computationally efficient statistical methods to measure latent factors (e.g. skills, abilities, and personalities) using large-scale assessment data. In addition to understanding the latent information, the covariate effect on responses controlling for latent factors is also of great scientific interest and has wide applications, such as evaluating the fairness of educational testing, where the covariate effect reflects whether a test question is biased toward certain individual characteristics (e.g. gender and race) taking into account their latent abilities. However, the large sample size, substantial covariate dimension, and great test length pose great challenges to developing efficient methods and drawing valid inferences. Moreover, to accommodate the commonly encountered discrete types of responses, nonlinear factor models (item response theory models) are often assumed, bringing in further complexity to the problem. To address these challenges, we consider a covariate-adjusted generalized factor model and develop novel and interpretable conditions to address the identifiability issue. Based on the identifiability conditions, we propose a joint maximum likelihood estimation method and establish estimation consistency and asymptotic normality results for the covariate effects under a practical yet challenging asymptotic regime. Furthermore, we derive estimation and inference results for latent factors and the factor loadings. We illustrate the performance of this method through extensive numerical studies and an application to a large-scale educational assessment, the Programme for International Student Assessment (PISA). This is a joint work with Jing Ouyang, Chengyu Cui, and Kean Ming Tan.