Literature DB >> 29795903

Three New Methods for Analysis of Answer Changes.

Sandip Sinharay1, Matthew S Johnson2.   

Abstract

In a pioneering research article, Wollack and colleagues suggested the "erasure detection index" (EDI) to detect test tampering. The EDI can be used with or without a continuity correction and is assumed to follow the standard normal distribution under the null hypothesis of no test tampering. When used without a continuity correction, the EDI often has inflated Type I error rates. When used with a continuity correction, the EDI has satisfactory Type I error rates, but smaller power compared with the EDI without a continuity correction. This article suggests three methods for detecting test tampering that do not rely on the assumption of a standard normal distribution under the null hypothesis. It is demonstrated in a detailed simulation study that the performance of each suggested method is slightly better than that of the EDI. The EDI and the suggested methods were applied to a real data set. The suggested methods, although more computation intensive than the EDI, seem to be promising in detecting test tampering.

Entities:  

Keywords:  Markov chain Monte Carlo; erasure analysis; generalized binomial model; test fraud; test security

Year:  2016        PMID: 29795903      PMCID: PMC5965521          DOI: 10.1177/0013164416632287

Source DB:  PubMed          Journal:  Educ Psychol Meas        ISSN: 0013-1644            Impact factor:   2.821


  1 in total

1.  Bayesian Checks on Cheating on Tests.

Authors:  Wim J van der Linden; Charles Lewis
Journal:  Psychometrika       Date:  2014-06-11       Impact factor: 2.500

  1 in total
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1.  The Use of Theory of Linear Mixed-Effects Models to Detect Fraudulent Erasures at an Aggregate Level.

Authors:  Luyao Peng; Sandip Sinharay
Journal:  Educ Psychol Meas       Date:  2021-03-29       Impact factor: 2.821

  1 in total

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