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The Impact of Measurement Noninvariance across Time and Group in Longitudinal Item Response Modeling
In-Hee Choi – Asia Pacific Education Review, 2024
Longitudinal item response data often exhibit two types of measurement noninvariance: the noninvariance of item parameters between subject groups and that of item parameters across multiple time points. This study proposes a comprehensive approach to the simultaneous modeling of both types of measurement noninvariance in terms of longitudinal item…
Descriptors: Longitudinal Studies, Item Response Theory, Growth Models, Error of Measurement
Janet Mee; Ravi Pandian; Justin Wolczynski; Amy Morales; Miguel Paniagua; Polina Harik; Peter Baldwin; Brian E. Clauser – Advances in Health Sciences Education, 2024
Recent advances in automated scoring technology have made it practical to replace multiple-choice questions (MCQs) with short-answer questions (SAQs) in large-scale, high-stakes assessments. However, most previous research comparing these formats has used small examinee samples testing under low-stakes conditions. Additionally, previous studies…
Descriptors: Multiple Choice Tests, High Stakes Tests, Test Format, Test Items
Bilal Ghanem; Alona Fyshe – International Educational Data Mining Society, 2024
Multiple choice questions (MCQs) are a common way to assess reading comprehension. Every MCQ needs a set of distractor answers that are incorrect, but plausible enough to test student knowledge. However, good distractors are hard to create. Distractor generation (DG) models have been proposed, and their performance is typically evaluated using…
Descriptors: Multiple Choice Tests, Reading Comprehension, Test Items, Testing
Daniel Lewis; Melanie Graw; Michael Baker – Journal of Applied Testing Technology, 2024
Embedded Standard Setting (ESS; Lewis & Cook, 2020) transforms standard setting from a standalone workshop to an active part of the assessment development lifecycle. ESS purports to lower costs by eliminating the standard-setting workshop and enhance the validity argument by maintaining a consistent focus on the evidentiary relationship…
Descriptors: Standard Setting (Scoring), Test Items, Test Construction, Food Service
Kasli, Murat; Zopluoglu, Cengiz; Toton, Sarah L. – Journal of Educational Measurement, 2023
Response times (RTs) have recently attracted a significant amount of attention in the literature as they may provide meaningful information about item preknowledge. In this study, a new model, the Deterministic Gated Lognormal Response Time (DG-LNRT) model, is proposed to identify examinees with item preknowledge using RTs. The proposed model was…
Descriptors: Reaction Time, Test Items, Models, Familiarity
Neuert, Cornelia E.; Meitinger, Katharina; Behr, Dorothée – Sociological Methods & Research, 2023
The method of web probing integrates cognitive interviewing techniques into web surveys and is increasingly used to evaluate survey questions. In a usual web probing scenario, probes are administered immediately after the question to be tested (concurrent probing), typically as open-ended questions. A second possibility of administering probes is…
Descriptors: Internet, Online Surveys, Test Items, Evaluation
He, Yinhong; Qi, Yuanyuan – Journal of Educational Measurement, 2023
In multidimensional computerized adaptive testing (MCAT), item selection strategies are generally constructed based on responses, and they do not consider the response times required by items. This study constructed two new criteria (referred to as DT-inc and DT) for MCAT item selection by utilizing information from response times. The new designs…
Descriptors: Reaction Time, Adaptive Testing, Computer Assisted Testing, Test Items
Ben Babcock; Kim Brunnert – Journal of Applied Testing Technology, 2023
Automatic Item Generation (AIG) is an extremely useful tool to construct many high-quality exam items more efficiently than traditional item writing methods. A large pool of items, however, presents challenges like identifying a particular item to meet a specific need. For example, when making a fixed form exam, best practices forbid item stems…
Descriptors: Test Items, Automation, Algorithms, Artificial Intelligence
Aiman Mohammad Freihat; Omar Saleh Bani Yassin – Educational Process: International Journal, 2025
Background/purpose: This study aimed to reveal the accuracy of estimation of multiple-choice test items parameters following the models of the item-response theory in measurement. Materials/methods: The researchers depended on the measurement accuracy indicators, which express the absolute difference between the estimated and actual values of the…
Descriptors: Accuracy, Computation, Multiple Choice Tests, Test Items
Patrik Havan; Michal Kohút; Peter Halama – International Journal of Testing, 2025
Acquiescence is the tendency of participants to shift their responses to agreement. Lechner et al. (2019) introduced the following mechanisms of acquiescence: social deference and cognitive processing. We added their interaction into a theoretical framework. The sample consists of 557 participants. We found significant medium strong relationship…
Descriptors: Cognitive Processes, Attention, Difficulty Level, Reflection
Sinan M. Bekmezci; Nuri Dogan – International Journal of Assessment Tools in Education, 2025
This study compares the psychometric properties of scales developed using Exploratory Factor Analysis (EFA), Self-Organizing Map (SOM), and Andrich's Rating Scale Model (RSM). Data for the research were collected by administering the "Statistical Attitude Scale" trial form, previously used in a separate study, to 808 individuals. First,…
Descriptors: Factor Analysis, Goodness of Fit, Attitude Measures, Test Items
Benjamin A. Motz; Anna L. Chinni; Audrey G. Barriball; Danielle S. McNamara – Grantee Submission, 2025
When learning with self-testing alone, will a learner make inferences between the tested items? This study examines whether self-testing's benefits extend beyond isolated facts to support broader connections between the facts. Comparing self-testing to self-explanation (a strategy known to facilitate inferential learning), we find that while…
Descriptors: Inferences, Testing, Test Items, Self Evaluation (Individuals)
David Hope; David Kluth; Matthew Homer; Avril Dewar; Rikki Goddard-Fuller; Alan Jaap; Helen Cameron – Advances in Health Sciences Education, 2025
Rasch modelling is a powerful tool for evaluating item performance, measuring drift in difficulty over time, and comparing students who sat assessments at different times or at different sites. Here, we use data from thirty UK medical schools to describe the benefits of Rasch modelling in quality assurance and the barriers to using it. Sixty…
Descriptors: Item Response Theory, Medical Schools, Foreign Countries, Quality Assurance
Ildiko Porter-Szucs; Cynthia J. Macknish; Suzanne Toohey – John Wiley & Sons, Inc, 2025
"A Practical Guide to Language Assessment" helps educators at every level redefine their approach to language assessment. Grounded in extensive research and aligned with the latest advances in language education, this comprehensive guide introduces foundational concepts and explores key principles in test development and item writing.…
Descriptors: Student Evaluation, Language Tests, Test Construction, Test Items
Brent A. Stevenor; Nadine LeBarron McBride; Charles Anyanwu – Journal of Applied Testing Technology, 2025
Enemy items are two test items that should not be presented to a candidate on the same test. Identifying enemies is essential for personnel assessment, as they weaken the measurement precision and validity of a test. In this research, we examined the effectiveness of lexical and semantic natural language processing techniques for identifying enemy…
Descriptors: Test Items, Natural Language Processing, Occupational Tests, Test Construction

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