Detection of two-way outliers in multivariate data and application to cheating detection in educational tests

Yunxiao Chen, Yan Lu and Irini Moustaki

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: 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.

Keywords: Bayesian hierarchical model; outlier detection; false discovery rate; compound decision; test fairness; item response theory; latent class analysis (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2022-09-01
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Annals of Applied Statistics, 1, September, 2022, 16(3), pp. 1718 - 1746. ISSN: 1932-6157

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