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Showing 1 to 15 of 51 results Save | Export
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He, Yinhong – Journal of Educational Measurement, 2023
Back random responding (BRR) behavior is one of the commonly observed careless response behaviors. Accurately detecting BRR behavior can improve test validities. Yu and Cheng (2019) showed that the change point analysis (CPA) procedure based on weighted residual (CPA-WR) performed well in detecting BRR. Compared with the CPA procedure, the…
Descriptors: Test Validity, Item Response Theory, Measurement, Monte Carlo Methods
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Novak, Josip; Rebernjak, Blaž – Measurement: Interdisciplinary Research and Perspectives, 2023
A Monte Carlo simulation study was conducted to examine the performance of [alpha], [lambda]2, [lambda][subscript 4], [lambda][subscript 2], [omega][subscript T], GLB[subscript MRFA], and GLB[subscript Algebraic] coefficients. Population reliability, distribution shape, sample size, test length, and number of response categories were varied…
Descriptors: Monte Carlo Methods, Evaluation Methods, Reliability, Simulation
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Shaojie Wang; Won-Chan Lee; Minqiang Zhang; Lixin Yuan – Applied Measurement in Education, 2024
To reduce the impact of parameter estimation errors on IRT linking results, recent work introduced two information-weighted characteristic curve methods for dichotomous items. These two methods showed outstanding performance in both simulation and pseudo-form pseudo-group analysis. The current study expands upon the concept of information…
Descriptors: Item Response Theory, Test Format, Test Length, Error of Measurement
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Basman, Munevver – International Journal of Assessment Tools in Education, 2023
To ensure the validity of the tests is to check that all items have similar results across different groups of individuals. However, differential item functioning (DIF) occurs when the results of individuals with equal ability levels from different groups differ from each other on the same test item. Based on Item Response Theory and Classic Test…
Descriptors: Test Bias, Test Items, Test Validity, Item Response Theory
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Erdem-Kara, Basak; Dogan, Nuri – International Journal of Assessment Tools in Education, 2022
Recently, adaptive test approaches have become a viable alternative to traditional fixed-item tests. The main advantage of adaptive tests is that they reach desired measurement precision with fewer items. However, fewer items mean that each item has a more significant effect on ability estimation and therefore those tests are open to more…
Descriptors: Item Analysis, Computer Assisted Testing, Test Items, Test Construction
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Kalkan, Ömür Kaya – Measurement: Interdisciplinary Research and Perspectives, 2022
The four-parameter logistic (4PL) Item Response Theory (IRT) model has recently been reconsidered in the literature due to the advances in the statistical modeling software and the recent developments in the estimation of the 4PL IRT model parameters. The current simulation study evaluated the performance of expectation-maximization (EM),…
Descriptors: Comparative Analysis, Sample Size, Test Length, Algorithms
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Yu, Albert; Douglas, Jeffrey A. – Journal of Educational and Behavioral Statistics, 2023
We propose a new item response theory growth model with item-specific learning parameters, or ISLP, and two variations of this model. In the ISLP model, either items or blocks of items have their own learning parameters. This model may be used to improve the efficiency of learning in a formative assessment. We show ways that the ISLP model's…
Descriptors: Item Response Theory, Learning, Markov Processes, Monte Carlo Methods
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Sedat Sen; Allan S. Cohen – Educational and Psychological Measurement, 2024
A Monte Carlo simulation study was conducted to compare fit indices used for detecting the correct latent class in three dichotomous mixture item response theory (IRT) models. Ten indices were considered: Akaike's information criterion (AIC), the corrected AIC (AICc), Bayesian information criterion (BIC), consistent AIC (CAIC), Draper's…
Descriptors: Goodness of Fit, Item Response Theory, Sample Size, Classification
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Baris Pekmezci, Fulya; Sengul Avsar, Asiye – International Journal of Assessment Tools in Education, 2021
There is a great deal of research about item response theory (IRT) conducted by simulations. Item and ability parameters are estimated with varying numbers of replications under different test conditions. However, it is not clear what the appropriate number of replications should be. The aim of the current study is to develop guidelines for the…
Descriptors: Item Response Theory, Computation, Accuracy, Monte Carlo Methods
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Wang, Shaojie; Zhang, Minqiang; Lee, Won-Chan; Huang, Feifei; Li, Zonglong; Li, Yixing; Yu, Sufang – Journal of Educational Measurement, 2022
Traditional IRT characteristic curve linking methods ignore parameter estimation errors, which may undermine the accuracy of estimated linking constants. Two new linking methods are proposed that take into account parameter estimation errors. The item- (IWCC) and test-information-weighted characteristic curve (TWCC) methods employ weighting…
Descriptors: Item Response Theory, Error of Measurement, Accuracy, Monte Carlo Methods
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Uysal, Ibrahim; Sahin-Kürsad, Merve; Kiliç, Abdullah Faruk – Participatory Educational Research, 2022
The aim of the study was to examine the common items in the mixed format (e.g., multiple-choices and essay items) contain parameter drifts in the test equating processes performed with the common item nonequivalent groups design. In this study, which was carried out using Monte Carlo simulation with a fully crossed design, the factors of test…
Descriptors: Test Items, Test Format, Item Response Theory, Equated Scores
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Arikan, Serkan; Aybek, Eren Can – Educational Measurement: Issues and Practice, 2022
Many scholars compared various item discrimination indices in real or simulated data. Item discrimination indices, such as item-total correlation, item-rest correlation, and IRT item discrimination parameter, provide information about individual differences among all participants. However, there are tests that aim to select a very limited number…
Descriptors: Monte Carlo Methods, Item Analysis, Correlation, Individual Differences
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Baris Pekmezci, Fulya; Gulleroglu, H. Deniz – Eurasian Journal of Educational Research, 2019
Purpose: This study aims to investigate the orthogonality assumption, which restricts the use of Bifactor item response theory under different conditions. Method: Data of the study have been obtained in accordance with the Bifactor model. It has been produced in accordance with two different models (Model 1 and Model 2) in a simulated way.…
Descriptors: Item Response Theory, Accuracy, Item Analysis, Correlation
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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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Yasuda, Jun-ichiro; Mae, Naohiro; Hull, Michael M.; Taniguchi, Masa-aki – Physical Review Physics Education Research, 2021
As a method to shorten the test time of the Force Concept Inventory (FCI), we suggest the use of computerized adaptive testing (CAT). CAT is the process of administering a test on a computer, with items (i.e., questions) selected based upon the responses of the examinee to prior items. In so doing, the test length can be significantly shortened.…
Descriptors: Foreign Countries, College Students, Student Evaluation, Computer Assisted Testing
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