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Sohee Kim; Ki Lynn Cole – International Journal of Testing, 2025
This study conducted a comprehensive comparison of Item Response Theory (IRT) linking methods applied to a bifactor model, examining their performance on both multiple choice (MC) and mixed format tests within the common item nonequivalent group design framework. Four distinct multidimensional IRT linking approaches were explored, consisting of…
Descriptors: Item Response Theory, Comparative Analysis, Models, Item Analysis
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Fu, Yanyan; Strachan, Tyler; Ip, Edward H.; Willse, John T.; Chen, Shyh-Huei; Ackerman, Terry – International Journal of Testing, 2020
This research examined correlation estimates between latent abilities when using the two-dimensional and three-dimensional compensatory and noncompensatory item response theory models. Simulation study results showed that the recovery of the latent correlation was best when the test contained 100% of simple structure items for all models and…
Descriptors: Item Response Theory, Models, Test Items, Simulation
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Bradshaw, Laine P.; Madison, Matthew J. – International Journal of Testing, 2016
In item response theory (IRT), the invariance property states that item parameter estimates are independent of the examinee sample, and examinee ability estimates are independent of the test items. While this property has long been established and understood by the measurement community for IRT models, the same cannot be said for diagnostic…
Descriptors: Classification, Models, Simulation, Psychometrics
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Wyse, Adam E.; Mapuranga, Raymond – International Journal of Testing, 2009
Differential item functioning (DIF) analysis is a statistical technique used for ensuring the equity and fairness of educational assessments. This study formulates a new DIF analysis method using the information similarity index (ISI). ISI compares item information functions when data fits the Rasch model. Through simulations and an international…
Descriptors: Test Bias, Evaluation Methods, Test Items, Educational Assessment
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Sese, Albert; Palmer, Alfonso L.; Montano, Juan J. – International Journal of Testing, 2004
The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…
Descriptors: Psychometrics, Computer Simulation, Models, Comparative Analysis