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

Chen, Yunxiao ORCID: 0000-0002-7215-2324, Lu, Yan and Moustaki, Irini ORCID: 0000-0001-8371-1251 (2022) Detection of two-way outliers in multivariate data and application to cheating detection in educational tests. Annals of Applied Statistics, 16 (3). 1718 - 1746. ISSN 1932-6157

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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.

Item Type:	Article
Official URL:	https://projecteuclid.org/journals/annals-of-appli...
Additional Information:	© 2021 Institute of Mathematical Statistics
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	26 Oct 2021 08:36
Last Modified:	20 Dec 2024 00:42
URI:	http://eprints.lse.ac.uk/id/eprint/112499

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