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Jin, Kuan-Yu; Siu, Wai-Lok; Huang, Xiaoting – Journal of Educational Measurement, 2022
Multiple-choice (MC) items are widely used in educational tests. Distractor analysis, an important procedure for checking the utility of response options within an MC item, can be readily implemented in the framework of item response theory (IRT). Although random guessing is a popular behavior of test-takers when answering MC items, none of the…
Descriptors: Guessing (Tests), Multiple Choice Tests, Item Response Theory, Attention
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Liu, Jinghua; Becker, Kirk – Journal of Educational Measurement, 2022
For any testing programs that administer multiple forms across multiple years, maintaining score comparability via equating is essential. With continuous testing and high-stakes results, especially with less secure online administrations, testing programs must consider the potential for cheating on their exams. This study used empirical and…
Descriptors: Cheating, Item Response Theory, Scores, High Stakes Tests
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Wyse, Adam E.; McBride, James R. – Journal of Educational Measurement, 2021
A key consideration when giving any computerized adaptive test (CAT) is how much adaptation is present when the test is used in practice. This study introduces a new framework to measure the amount of adaptation of Rasch-based CATs based on looking at the differences between the selected item locations (Rasch item difficulty parameters) of the…
Descriptors: Item Response Theory, Computer Assisted Testing, Adaptive Testing, Test Items
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Clauser, Brian E.; Kane, Michael; Clauser, Jerome C. – Journal of Educational Measurement, 2020
An Angoff standard setting study generally yields judgments on a number of items by a number of judges (who may or may not be nested in panels). Variability associated with judges (and possibly panels) contributes error to the resulting cut score. The variability associated with items plays a more complicated role. To the extent that the mean item…
Descriptors: Cutting Scores, Generalization, Decision Making, Standard Setting
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Albano, Anthony D.; Cai, Liuhan; Lease, Erin M.; McConnell, Scott R. – Journal of Educational Measurement, 2019
Studies have shown that item difficulty can vary significantly based on the context of an item within a test form. In particular, item position may be associated with practice and fatigue effects that influence item parameter estimation. The purpose of this research was to examine the relevance of item position specifically for assessments used in…
Descriptors: Test Items, Computer Assisted Testing, Item Analysis, Difficulty Level
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Ames, Allison; Smith, Elizabeth – Journal of Educational Measurement, 2018
Bayesian methods incorporate model parameter information prior to data collection. Eliciting information from content experts is an option, but has seen little implementation in Bayesian item response theory (IRT) modeling. This study aims to use ethical reasoning content experts to elicit prior information and incorporate this information into…
Descriptors: Item Response Theory, Bayesian Statistics, Ethics, Specialists
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Fitzpatrick, Joseph; Skorupski, William P. – Journal of Educational Measurement, 2016
The equating performance of two internal anchor test structures--miditests and minitests--is studied for four IRT equating methods using simulated data. Originally proposed by Sinharay and Holland, miditests are anchors that have the same mean difficulty as the overall test but less variance in item difficulties. Four popular IRT equating methods…
Descriptors: Difficulty Level, Test Items, Comparative Analysis, Test Construction
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Jin, Kuan-Yu; Wang, Wen-Chung – Journal of Educational Measurement, 2014
Sometimes, test-takers may not be able to attempt all items to the best of their ability (with full effort) due to personal factors (e.g., low motivation) or testing conditions (e.g., time limit), resulting in poor performances on certain items, especially those located toward the end of a test. Standard item response theory (IRT) models fail to…
Descriptors: Student Evaluation, Item Response Theory, Models, Simulation
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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
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Veldkamp, Bernard P. – Journal of Educational Measurement, 2016
Many standardized tests are now administered via computer rather than paper-and-pencil format. The computer-based delivery mode brings with it certain advantages. One advantage is the ability to adapt the difficulty level of the test to the ability level of the test taker in what has been termed computerized adaptive testing (CAT). A second…
Descriptors: Computer Assisted Testing, Reaction Time, Standardized Tests, Difficulty Level
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Zhang, Jinming; Li, Jie – Journal of Educational Measurement, 2016
An IRT-based sequential procedure is developed to monitor items for enhancing test security. The procedure uses a series of statistical hypothesis tests to examine whether the statistical characteristics of each item under inspection have changed significantly during CAT administration. This procedure is compared with a previously developed…
Descriptors: Computer Assisted Testing, Test Items, Difficulty Level, Item Response Theory
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Schroeders, Ulrich; Robitzsch, Alexander; Schipolowski, Stefan – Journal of Educational Measurement, 2014
C-tests are a specific variant of cloze tests that are considered time-efficient, valid indicators of general language proficiency. They are commonly analyzed with models of item response theory assuming local item independence. In this article we estimated local interdependencies for 12 C-tests and compared the changes in item difficulties,…
Descriptors: Comparative Analysis, Psychometrics, Cloze Procedure, Language Tests
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Li, Feiming; Cohen, Allan; Shen, Linjun – Journal of Educational Measurement, 2012
Computer-based tests (CBTs) often use random ordering of items in order to minimize item exposure and reduce the potential for answer copying. Little research has been done, however, to examine item position effects for these tests. In this study, different versions of a Rasch model and different response time models were examined and applied to…
Descriptors: Computer Assisted Testing, Test Items, Item Response Theory, Models
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Jiao, Hong; Kamata, Akihito; Wang, Shudong; Jin, Ying – Journal of Educational Measurement, 2012
The applications of item response theory (IRT) models assume local item independence and that examinees are independent of each other. When a representative sample for psychometric analysis is selected using a cluster sampling method in a testlet-based assessment, both local item dependence and local person dependence are likely to be induced.…
Descriptors: Item Response Theory, Test Items, Markov Processes, Monte Carlo Methods
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Jiao, Hong; Wang, Shudong; He, Wei – Journal of Educational Measurement, 2013
This study demonstrated the equivalence between the Rasch testlet model and the three-level one-parameter testlet model and explored the Markov Chain Monte Carlo (MCMC) method for model parameter estimation in WINBUGS. The estimation accuracy from the MCMC method was compared with those from the marginalized maximum likelihood estimation (MMLE)…
Descriptors: Computation, Item Response Theory, Models, Monte Carlo Methods
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