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Showing 1 to 15 of 55 results Save | Export
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Kuan-Yu Jin; Wai-Lok Siu – Journal of Educational Measurement, 2025
Educational tests often have a cluster of items linked by a common stimulus ("testlet"). In such a design, the dependencies caused between items are called "testlet effects." In particular, the directional testlet effect (DTE) refers to a recursive influence whereby responses to earlier items can positively or negatively affect…
Descriptors: Models, Test Items, Educational Assessment, Scores
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Metsämuuronen, Jari – Practical Assessment, Research & Evaluation, 2022
This article discusses visual techniques for detecting test items that would be optimal to be selected to the final compilation on the one hand and, on the other hand, to out-select those items that would lower the quality of the compilation. Some classic visual tools are discussed, first, in a practical manner in diagnosing the logical,…
Descriptors: Test Items, Item Analysis, Item Response Theory, Cutting Scores
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Ö. Emre C. Alagöz; Thorsten Meiser – Educational and Psychological Measurement, 2024
To improve the validity of self-report measures, researchers should control for response style (RS) effects, which can be achieved with IRTree models. A traditional IRTree model considers a response as a combination of distinct decision-making processes, where the substantive trait affects the decision on response direction, while decisions about…
Descriptors: Item Response Theory, Validity, Self Evaluation (Individuals), Decision Making
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Özdogan, Didem; Kelecioglu, Hülya – International Journal of Assessment Tools in Education, 2022
This study aims to analyze the differential bundle functioning in multidimensional tests with a specific purpose to detect this effect through differentiating the location of the item with DIF in the test, the correlation between the dimensions, the sample size, and the ratio of reference to focal group size. The first 10 items of the test that is…
Descriptors: Correlation, Sample Size, Test Items, Item Analysis
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Kyung-Mi O. – Language Testing in Asia, 2024
This study examines the efficacy of artificial intelligence (AI) in creating parallel test items compared to human-made ones. Two test forms were developed: one consisting of 20 existing human-made items and another with 20 new items generated with ChatGPT assistance. Expert reviews confirmed the content parallelism of the two test forms.…
Descriptors: Comparative Analysis, Artificial Intelligence, Computer Software, Test Items
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Fuchimoto, Kazuma; Ishii, Takatoshi; Ueno, Maomi – IEEE Transactions on Learning Technologies, 2022
Educational assessments often require uniform test forms, for which each test form has equivalent measurement accuracy but with a different set of items. For uniform test assembly, an important issue is the increase of the number of assembled uniform tests. Although many automatic uniform test assembly methods exist, the maximum clique algorithm…
Descriptors: Simulation, Efficiency, Test Items, Educational Assessment
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Roger Young; Emily Courtney; Alexander Kah; Mariah Wilkerson; Yi-Hsin Chen – Teaching of Psychology, 2025
Background: Multiple-choice item (MCI) assessments are burdensome for instructors to develop. Artificial intelligence (AI, e.g., ChatGPT) can streamline the process without sacrificing quality. The quality of AI-generated MCIs and human experts is comparable. However, whether the quality of AI-generated MCIs is equally good across various domain-…
Descriptors: Item Response Theory, Multiple Choice Tests, Psychology, Textbooks
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Mimi Ismail; Ahmed Al - Badri; Said Al - Senaidi – Journal of Education and e-Learning Research, 2025
This study aimed to reveal the differences in individuals' abilities, their standard errors, and the psychometric properties of the test according to the two methods of applying the test (electronic and paper). The descriptive approach was used to achieve the study's objectives. The study sample consisted of 74 male and female students at the…
Descriptors: Achievement Tests, Computer Assisted Testing, Psychometrics, Item Response Theory
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Nagy, Gabriel; Ulitzsch, Esther – Educational and Psychological Measurement, 2022
Disengaged item responses pose a threat to the validity of the results provided by large-scale assessments. Several procedures for identifying disengaged responses on the basis of observed response times have been suggested, and item response theory (IRT) models for response engagement have been proposed. We outline that response time-based…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Predictor Variables, Classification
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Saatcioglu, Fatima Munevver; Atar, Hakan Yavuz – International Journal of Assessment Tools in Education, 2022
This study aims to examine the effects of mixture item response theory (IRT) models on item parameter estimation and classification accuracy under different conditions. The manipulated variables of the simulation study are set as mixture IRT models (Rasch, 2PL, 3PL); sample size (600, 1000); the number of items (10, 30); the number of latent…
Descriptors: Accuracy, Classification, Item Response Theory, Programming Languages
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Zheng, Xiaying; Yang, Ji Seung – Measurement: Interdisciplinary Research and Perspectives, 2021
The purpose of this paper is to briefly introduce two most common applications of multiple group item response theory (IRT) models, namely detecting differential item functioning (DIF) analysis and nonequivalent group score linking with a simultaneous calibration. We illustrate how to conduct those analyses using the "Stata" item…
Descriptors: Item Response Theory, Test Bias, Computer Software, Statistical Analysis
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Luo, Yong – Educational and Psychological Measurement, 2018
Mplus is a powerful latent variable modeling software program that has become an increasingly popular choice for fitting complex item response theory models. In this short note, we demonstrate that the two-parameter logistic testlet model can be estimated as a constrained bifactor model in Mplus with three estimators encompassing limited- and…
Descriptors: Computer Software, Models, Statistical Analysis, Computation
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PaaBen, Benjamin; Dywel, Malwina; Fleckenstein, Melanie; Pinkwart, Niels – International Educational Data Mining Society, 2022
Item response theory (IRT) is a popular method to infer student abilities and item difficulties from observed test responses. However, IRT struggles with two challenges: How to map items to skills if multiple skills are present? And how to infer the ability of new students that have not been part of the training data? Inspired by recent advances…
Descriptors: Item Response Theory, Test Items, Item Analysis, Inferences
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Ames, Allison J.; Leventhal, Brian C.; Ezike, Nnamdi C. – Measurement: Interdisciplinary Research and Perspectives, 2020
Data simulation and Monte Carlo simulation studies are important skills for researchers and practitioners of educational and psychological measurement, but there are few resources on the topic specific to item response theory. Even fewer resources exist on the statistical software techniques to implement simulation studies. This article presents…
Descriptors: Monte Carlo Methods, Item Response Theory, Simulation, Computer Software
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von Davier, Matthias; Tyack, Lillian; Khorramdel, Lale – Educational and Psychological Measurement, 2023
Automated scoring of free drawings or images as responses has yet to be used in large-scale assessments of student achievement. In this study, we propose artificial neural networks to classify these types of graphical responses from a TIMSS 2019 item. We are comparing classification accuracy of convolutional and feed-forward approaches. Our…
Descriptors: Scoring, Networks, Artificial Intelligence, Elementary Secondary Education
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