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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
Chenchen Ma; Gongjun Xu – Grantee Submission, 2022
Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models widely used in educational, psychological and social sciences. In many applications of CDMs, certain hierarchical structures among the latent attributes are assumed by researchers to characterize their dependence structure. Specifically, a directed acyclic…
Descriptors: Vertical Organization, Models, Evaluation, Statistical Analysis
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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
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Schroeders, Ulrich; Schmidt, Christoph; Gnambs, Timo – Educational and Psychological Measurement, 2022
Careless responding is a bias in survey responses that disregards the actual item content, constituting a threat to the factor structure, reliability, and validity of psychological measurements. Different approaches have been proposed to detect aberrant responses such as probing questions that directly assess test-taking behavior (e.g., bogus…
Descriptors: Response Style (Tests), Surveys, Artificial Intelligence, Identification
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
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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
Sinharay, Sandip; Johnson, Matthew S. – Grantee Submission, 2021
Score differencing is one of 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
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Demirkaya, Onur; Bezirhan, Ummugul; Zhang, Jinming – Journal of Educational and Behavioral Statistics, 2023
Examinees with item preknowledge tend to obtain inflated test scores that undermine test score validity. With the availability of process data collected in computer-based assessments, the research on detecting item preknowledge has progressed on using both item scores and response times. Item revisit patterns of examinees can also be utilized as…
Descriptors: Test Items, Prior Learning, Knowledge Level, Reaction Time
Wang, Chun; Lu, Jing – Journal of Educational and Behavioral Statistics, 2021
In cognitive diagnostic assessment, multiple fine-grained attributes are measured simultaneously. Attribute hierarchies are considered important structural features of cognitive diagnostic models (CDMs) that provide useful information about the nature of attributes. Templin and Bradshaw first introduced a hierarchical diagnostic classification…
Descriptors: Cognitive Measurement, Models, Vertical Organization, Classification
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Seftor, Neil; Shannon, Lisa; Wilkerson, Stephanie; Klute, Mary – Regional Educational Laboratory Appalachia, 2021
Classification and Regression Tree (CART) analysis is a statistical modeling approach that uses quantitative data to predict future outcomes by generating decision trees. CART analysis can be useful for educators to inform their decision-making. For example, educators can use a decision tree from a CART analysis to identify students who are most…
Descriptors: Flow Charts, Decision Making, Statistical Analysis, Data Use
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Nuijten, Michèle B.; Polanin, Joshua R. – Research Synthesis Methods, 2020
We present the R package and web app "statcheck" to automatically detect statistical reporting inconsistencies in primary studies and meta-analyses. Previous research has shown a high prevalence of reported p-values that are inconsistent--meaning a re-calculated p-value, based on the reported test statistic and degrees of freedom, does…
Descriptors: Meta Analysis, Statistical Analysis, Reliability, Replication (Evaluation)
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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)
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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
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Johnson, Bettie Obi; Golonka, Annette M.; Emmanuel, Khalisha A.; Ponnarasu, Jeffshan J.; Pryor, Joseph E. – Journal of Chemical Education, 2022
A novel undergraduate analytical chemistry experiment involving the identification and quantification of linalool in essential oils was developed. The experiment uses the internal standard method with headspace solid phase microextraction (SPME) and gas chromatography--mass spectrometry (GC-MS). This method is applicable to any linalool-containing…
Descriptors: College Science, Chemistry, Science Experiments, Methods
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