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Grochowalski, Joseph H.; Hendrickson, Amy – Journal of Educational Measurement, 2023
Test takers wishing to gain an unfair advantage often share answers with other test takers, either sharing all answers (a full key) or some (a partial key). Detecting key sharing during a tight testing window requires an efficient, easily interpretable, and rich form of analysis that is descriptive and inferential. We introduce a detection method…
Descriptors: Identification, Cooperative Learning, Cheating, Statistical Analysis
Becker, Kirk; Meng, Huijuan – Journal of Applied Testing Technology, 2022
The rise of online proctoring potentially provides more opportunities for item harvesting and consequent brain dumping and shared "study guides" based on stolen content. This has increased the need for rapid approaches for evaluating and acting on suspicious test responses in every delivery modality. Both hiring proxy test takers and…
Descriptors: Identification, Cheating, Computer Assisted Testing, Observation
The Use of Theory of Linear Mixed-Effects Models to Detect Fraudulent Erasures at an Aggregate Level
Peng, Luyao; Sinharay, Sandip – Educational and Psychological Measurement, 2022
Wollack et al. (2015) suggested the erasure detection index (EDI) for detecting fraudulent erasures for individual examinees. Wollack and Eckerly (2017) and Sinharay (2018) extended the index of Wollack et al. (2015) to suggest three EDIs for detecting fraudulent erasures at the aggregate or group level. This article follows up on the research of…
Descriptors: Cheating, Identification, Statistical Analysis, Testing
Sinharay, Sandip; Johnson, Matthew S. – Journal of Educational and Behavioral Statistics, 2021
Score differencing is one of the six categories of statistical methods used to detect test fraud (Wollack & Schoenig, 2018) and involves the testing of the null hypothesis that the performance of an examinee is similar over two item sets versus the alternative hypothesis that the performance is better on one of the item sets. We suggest, to…
Descriptors: Probability, Bayesian Statistics, Cheating, Statistical Analysis
Ucar, Arzu; Dogan, Celal Deha – International Journal of Assessment Tools in Education, 2021
Distance learning has become a popular phenomenon across the world during the COVID-19 pandemic. This led to answer copying behavior among individuals. The cut point of the Kullback-Leibler Divergence (KL) method, one of the copy detecting methods, was calculated using the Youden Index, Cost-Benefit, and Min Score p-value approaches. Using the cut…
Descriptors: Cheating, Identification, Cutting Scores, Statistical Analysis
He, Qingping; Meadows, Michelle; Black, Beth – Research Papers in Education, 2022
A potential negative consequence of high-stakes testing is inappropriate test behaviour involving individuals and/or institutions. Inappropriate test behaviour and test collusion can result in aberrant response patterns and anomalous test scores and invalidate the intended interpretation and use of test results. A variety of statistical techniques…
Descriptors: Statistical Analysis, High Stakes Tests, Scores, Response Style (Tests)
Wang, Xi; Liu, Yang – Journal of Educational and Behavioral Statistics, 2020
In continuous testing programs, some items are repeatedly used across test administrations, and statistical methods are often used to evaluate whether items become compromised due to examinees' preknowledge. In this study, we proposed a residual method to detect compromised items when a test can be partitioned into two subsets of items: secure…
Descriptors: Test Items, Information Security, Error of Measurement, Cheating
Wang, Xi; Liu, Yang; Robin, Frederic; Guo, Hongwen – International Journal of Testing, 2019
In an on-demand testing program, some items are repeatedly used across test administrations. This poses a risk to test security. In this study, we considered a scenario wherein a test was divided into two subsets: one consisting of secure items and the other consisting of possibly compromised items. In a simulation study of multistage adaptive…
Descriptors: Identification, Methods, Test Items, Cheating
Man, Kaiwen; Harring, Jeffrey R. – Educational and Psychological Measurement, 2021
Many approaches have been proposed to jointly analyze item responses and response times to understand behavioral differences between normally and aberrantly behaved test-takers. Biometric information, such as data from eye trackers, can be used to better identify these deviant testing behaviors in addition to more conventional data types. Given…
Descriptors: Cheating, Item Response Theory, Reaction Time, Eye Movements
Sunbul, Onder; Yormaz, Seha – International Journal of Evaluation and Research in Education, 2018
In this study Type I Error and the power rates of omega (?) and GBT (generalized binomial test) indices were investigated for several nominal alpha levels and for 40 and 80-item test lengths with 10,000-examinee sample size under several test level restrictions. As a result, Type I error rates of both indices were found to be below the acceptable…
Descriptors: Difficulty Level, Cheating, Duplication, Test Length
Sunbul, Onder; Yormaz, Seha – Eurasian Journal of Educational Research, 2018
Purpose: Several studies can be found in the literature that investigate the performance of ? under various conditions. However no study for the effects of item difficulty, item discrimination, and ability restrictions on the performance of ? could be found. The current study aims to investigate the performance of ? for the conditions given below.…
Descriptors: Test Items, Difficulty Level, Ability, Cheating
Yormaz, Seha; Sünbül, Önder – Educational Sciences: Theory and Practice, 2017
This study aims to determine the Type I error rates and power of S[subscript 1] , S[subscript 2] indices and kappa statistic at detecting copying on multiple-choice tests under various conditions. It also aims to determine how copying groups are created in order to calculate how kappa statistics affect Type I error rates and power. In this study,…
Descriptors: Statistical Analysis, Cheating, Multiple Choice Tests, Sample Size
Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2017
An increasing concern of producers of educational assessments is fraudulent behavior during the assessment (van der Linden, 2009). Benefiting from item preknowledge (e.g., Eckerly, 2017; McLeod, Lewis, & Thissen, 2003) is one type of fraudulent behavior. This article suggests two new test statistics for detecting individuals who may have…
Descriptors: Test Items, Cheating, Testing Problems, Identification
Liebler, Robert – College Student Journal, 2017
Data from published studies is used to recover information about the sample mean self-reported number of times cheated by college students. The sample means were estimated by fitting distributions to the reported data. The few estimated sample means thus recovered were roughly 2 or less.
Descriptors: Cheating, Student Behavior, Undergraduate Students, Student Surveys
Man, Kaiwen; Harring, Jeffery R.; Ouyang, Yunbo; Thomas, Sarah L. – International Journal of Testing, 2018
Many important high-stakes decisions--college admission, academic performance evaluation, and even job promotion--depend on accurate and reliable scores from valid large-scale assessments. However, examinees sometimes cheat by copying answers from other test-takers or practicing with test items ahead of time, which can undermine the effectiveness…
Descriptors: Reaction Time, High Stakes Tests, Test Wiseness, Cheating