Accounting for Measurement Bias: A New Framework for Reliable Country Ranking in Large-Scale Educational Assessments

• 成果类型: Article; Early Access
• 署名作者: Ouyang, Jing; Chen, Yunxiao; Li, Chengcheng; Xu, Gongjun
• 署名单位: University of Hong Kong; University of London; London School Economics & Political Science; Microsoft; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2670732
• 发表日期: 2026-06-19
• 关键词: differential item functioning; item response theory; Latent Variable Models; Measurement invariance; psychometrics; ITEM-FOCUSED TREES; DIF DETECTION; maximum-likelihood; variable selection; purification; linking; models

摘要

International Large-scale Assessments (ILSAs), such as the Program for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS), are cornerstone tools for global educational research and policy-making. By benchmarking educational quality and performance trends, these assessments enable countries to evaluate and share effective pedagogical structures. Specifically, ILSAs employ Item Response Theory (IRT) models to rank countries by students' performance on cognitive items. However, measurement bias-arising from linguistic, cultural, and curricular differences-poses a significant threat to the statistical inference of IRT models and, consequently, the validity of the resulting rankings. Neglecting this bias can lead to systematic errors in parameter estimation, ultimately distorting national standings. To address this, we propose a novel method that avoids the restrictive assumptions typical of existing approaches, such as the prior identification of unbiased anchor items or designated reference groups. Our approach is computationally efficient and provides theoretical guarantees for the reliable recovery of group rankings. We apply this method to PISA 2022 data across the mathematics, science, and reading domains, yielding corrected performance rankings and insights into the survey's measurement-bias structures. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.