Seminar in Psychometrics

COMPS seminar main page

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

Date and time: May 10, 2022 (3:40 PM CET)
Place K4 at MFF UK, Sokolovká 83, Prague 8, also on Zoom

Abstract. In the talk we will discuss a 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. Covariates are also added to enhance the classification power of the model. 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.

References.
Yunxiao Chen, Yan Lu, & Irini Moustaki. Detection of two-way outliers in multivariate data and application to cheating detection in educational tests. Annals of Applied Statistics (In press). arXiv preprint 1911.09408