DETECTION OF TWO-WAY OUTLIERS IN MULTIVARIATE DATA AND APPLICATION TO CHEATING DETECTION IN EDUCATIONAL TESTS

• 成果类型: Article
• 署名作者: Chen, Yunxiao; Lu, Yan; Moustaki, Irini
• 署名单位: University of London; London School Economics & Political Science
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1564
• 发表日期: 2022
• 页码: 1718-1746
• 关键词: false discovery rate; EMPIRICAL BAYES; item preknowledge; MODEL; nonresponse; parameters; patterns

摘要

The paper proposes a new latent variable model for the simultaneous (two-way) detection of outlying individuals and items for item-response-type data. The proposed model is a synergy between a factor model for binary responses and continuous response times that captures normal item response behaviour and a latent class model that captures the outlying individuals and items. A statistical decision framework is developed under the proposed model that provides compound decision rules for controlling local false discovery/nondiscovery rates of outlier detection. Statistical inference is carried out under a Bayesian framework for which a Markov chain Monte Carlo algorithm is developed. The proposed method is applied to the detection of cheating in educational tests, due to item leakage, using a case study of a computer-based nonadaptive licensure assessment. The performance of the proposed method is evaluated by simulation studies.