NotesFAQContact Us
Collection
Advanced
Search Tips
Showing 1 to 15 of 28 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Li, Dongmei – Journal of Educational Measurement, 2022
Equating error is usually small relative to the magnitude of measurement error, but it could be one of the major sources of error contributing to mean scores of large groups in educational measurement, such as the year-to-year state mean score fluctuations. Though testing programs may routinely calculate the standard error of equating (SEE), the…
Descriptors: Error Patterns, Educational Testing, Group Testing, Statistical Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Puhan, Gautam; Kim, Sooyeon – Journal of Educational Measurement, 2022
As a result of the COVID-19 pandemic, at-home testing has become a popular delivery mode in many testing programs. When programs offer at-home testing to expand their service, the score comparability between test takers testing remotely and those testing in a test center is critical. This article summarizes statistical procedures that could be…
Descriptors: Scores, Scoring, Comparative Analysis, Testing
Peer reviewed Peer reviewed
Direct linkDirect link
Lin, Chih-Kai; Zhang, Jinming – Journal of Educational Measurement, 2018
Under the generalizability-theory (G-theory) framework, the estimation precision of variance components (VCs) is of significant importance in that they serve as the foundation of estimating reliability. Zhang and Lin advanced the discussion of nonadditivity in data from a theoretical perspective and showed the adverse effects of nonadditivity on…
Descriptors: Generalizability Theory, Reliability, Computation, Statistical Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Sinharay, Sandip; Wan, Ping; Choi, Seung W.; Kim, Dong-In – Journal of Educational Measurement, 2015
With an increase in the number of online tests, the number of interruptions during testing due to unexpected technical issues seems to be on the rise. For example, interruptions occurred during several recent state tests. When interruptions occur, it is important to determine the extent of their impact on the examinees' scores. Researchers such as…
Descriptors: Computer Assisted Testing, Testing Problems, Scores, Statistical Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Sinharay, Sandip; Duong, Minh Q.; Wood, Scott W. – Journal of Educational Measurement, 2017
As noted by Fremer and Olson, analysis of answer changes is often used to investigate testing irregularities because the analysis is readily performed and has proven its value in practice. Researchers such as Belov, Sinharay and Johnson, van der Linden and Jeon, van der Linden and Lewis, and Wollack, Cohen, and Eckerly have suggested several…
Descriptors: Identification, Statistics, Change, Tests
Peer reviewed Peer reviewed
Direct linkDirect link
Guo, Hongwen; Robin, Frederic; Dorans, Neil – Journal of Educational Measurement, 2017
The early detection of item drift is an important issue for frequently administered testing programs because items are reused over time. Unfortunately, operational data tend to be very sparse and do not lend themselves to frequent monitoring analyses, particularly for on-demand testing. Building on existing residual analyses, the authors propose…
Descriptors: Testing, Test Items, Identification, Sample Size
Peer reviewed Peer reviewed
Direct linkDirect link
Sinharay, Sandip; Wan, Ping; Whitaker, Mike; Kim, Dong-In; Zhang, Litong; Choi, Seung W. – Journal of Educational Measurement, 2014
With an increase in the number of online tests, interruptions during testing due to unexpected technical issues seem unavoidable. For example, interruptions occurred during several recent state tests. When interruptions occur, it is important to determine the extent of their impact on the examinees' scores. There is a lack of research on this…
Descriptors: Computer Assisted Testing, Testing Problems, Scores, Regression (Statistics)
Peer reviewed Peer reviewed
Direct linkDirect link
Belov, Dmitry I. – Journal of Educational Measurement, 2013
The development of statistical methods for detecting test collusion is a new research direction in the area of test security. Test collusion may be described as large-scale sharing of test materials, including answers to test items. Current methods of detecting test collusion are based on statistics also used in answer-copying detection.…
Descriptors: Cheating, Computer Assisted Testing, Adaptive Testing, Statistical Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Kim, Sooyeon; Moses, Tim; Yoo, Hanwook – Journal of Educational Measurement, 2015
This inquiry is an investigation of item response theory (IRT) proficiency estimators' accuracy under multistage testing (MST). We chose a two-stage MST design that includes four modules (one at Stage 1, three at Stage 2) and three difficulty paths (low, middle, high). We assembled various two-stage MST panels (i.e., forms) by manipulating two…
Descriptors: Comparative Analysis, Item Response Theory, Computation, Accuracy
Peer reviewed Peer reviewed
Direct linkDirect link
Debeer, Dries; Janssen, Rianne; De Boeck, Paul – Journal of Educational Measurement, 2017
When dealing with missing responses, two types of omissions can be discerned: items can be skipped or not reached by the test taker. When the occurrence of these omissions is related to the proficiency process the missingness is nonignorable. The purpose of this article is to present a tree-based IRT framework for modeling responses and omissions…
Descriptors: Item Response Theory, Test Items, Responses, Testing Problems
Peer reviewed Peer reviewed
Direct linkDirect link
Han, Kyung T. – Journal of Educational Measurement, 2012
Successful administration of computerized adaptive testing (CAT) programs in educational settings requires that test security and item exposure control issues be taken seriously. Developing an item selection algorithm that strikes the right balance between test precision and level of item pool utilization is the key to successful implementation…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Selection
Peer reviewed Peer reviewed
Direct linkDirect link
Wang, Chun; Chang, Hua-Hua; Huebner, Alan – Journal of Educational Measurement, 2011
This paper proposes two new item selection methods for cognitive diagnostic computerized adaptive testing: the restrictive progressive method and the restrictive threshold method. They are built upon the posterior weighted Kullback-Leibler (KL) information index but include additional stochastic components either in the item selection index or in…
Descriptors: Test Items, Adaptive Testing, Computer Assisted Testing, Cognitive Tests
Peer reviewed Peer reviewed
Direct linkDirect link
Puhan, Gautam – Journal of Educational Measurement, 2012
Tucker and chained linear equatings were evaluated in two testing scenarios. In Scenario 1, referred to as rater comparability scoring and equating, the anchor-to-total correlation is often very high for the new form but moderate for the reference form. This may adversely affect the results of Tucker equating, especially if the new and reference…
Descriptors: Testing, Scoring, Equated Scores, Statistical Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Armstrong, Ronald D.; Shi, Min – Journal of Educational Measurement, 2009
This article demonstrates the use of a new class of model-free cumulative sum (CUSUM) statistics to detect person fit given the responses to a linear test. The fundamental statistic being accumulated is the likelihood ratio of two probabilities. The detection performance of this CUSUM scheme is compared to other model-free person-fit statistics…
Descriptors: Probability, Simulation, Models, Psychometrics
Previous Page | Next Page ยป
Pages: 1  |  2