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AUEB Stats Seminars 6/5/2022: Detection of two-way outliers in multivariate data and  application to cheating detection in educational tests by Irini Moustaki (LSE) Forumgrstats

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Λέσχη Φίλων Στατιστικής - GrStats forum
AUEB Stats Seminars 6/5/2022: Detection of two-way outliers in multivariate data and  application to cheating detection in educational tests by Irini Moustaki (LSE) Forumgrstats
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AUEB Stats Seminars 6/5/2022: Detection of two-way outliers in multivariate data and  application to cheating detection in educational tests by Irini Moustaki (LSE) Empty AUEB Stats Seminars 6/5/2022: Detection of two-way outliers in multivariate data and application to cheating detection in educational tests by Irini Moustaki (LSE)

Thu 5 May 2022 - 16:01
AUEB Stats Seminars 6/5/2022: Detection of two-way outliers in multivariate data and  application to cheating detection in educational tests by Irini Moustaki (LSE) L3cdtk10


To σεμινάριο της Παρασκευής του Τμήματος Στατιστικής ΟΠΑ

Presenter: Irini Moustaki
Department of Statistics, London School of Economics & Political Science, UK

Date: Friday 6/5/2022,  13.00 (Athens time) 11:00 am (UK time)

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

Co-authors: Yunxiao Chen and Yan Lu


Link: https://teams.microsoft.com/l/meetup-join/19%3a0fe3bd7e094a4ccfacecb92ce36cfe69%40thread.tacv2/1643626227317?context=%7b%22Tid%22%3a%22ad5ba4a2-7857-4ea1-895e-b3d5207a174f%22%2c%22Oid%22%3a%22381c5dc1-ed77-403a-9d53-e2db51c33563%22%7d

facebook event: https://www.facebook.com/events/994533764808360/
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