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Dubravka Svetina Valdivia; Shenghai Dai – Journal of Experimental Education, 2024
Applications of polytomous IRT models in applied fields (e.g., health, education, psychology) are abound. However, little is known about the impact of the number of categories and sample size requirements for precise parameter recovery. In a simulation study, we investigated the impact of the number of response categories and required sample size…
Descriptors: Item Response Theory, Sample Size, Models, Classification
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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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Lathrop, Quinn N.; Cheng, Ying – Applied Psychological Measurement, 2013
Within the framework of item response theory (IRT), there are two recent lines of work on the estimation of classification accuracy (CA) rate. One approach estimates CA when decisions are made based on total sum scores, the other based on latent trait estimates. The former is referred to as the Lee approach, and the latter, the Rudner approach,…
Descriptors: Item Response Theory, Accuracy, Classification, Computation
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Svetina, Dubravka – Educational and Psychological Measurement, 2013
The purpose of this study was to investigate the effect of complex structure on dimensionality assessment in noncompensatory multidimensional item response models using dimensionality assessment procedures based on DETECT (dimensionality evaluation to enumerate contributing traits) and NOHARM (normal ogive harmonic analysis robust method). Five…
Descriptors: Item Response Theory, Statistical Analysis, Computation, Test Length
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Li, Ying; Rupp, Andre A. – Educational and Psychological Measurement, 2011
This study investigated the Type I error rate and power of the multivariate extension of the S - [chi][squared] statistic using unidimensional and multidimensional item response theory (UIRT and MIRT, respectively) models as well as full-information bifactor (FI-bifactor) models through simulation. Manipulated factors included test length, sample…
Descriptors: Test Length, Item Response Theory, Statistical Analysis, Error Patterns