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Wang, Wen-Chung; Liu, Chen-Wei; Wu, Shiu-Lien – Applied Psychological Measurement, 2013
The random-threshold generalized unfolding model (RTGUM) was developed by treating the thresholds in the generalized unfolding model as random effects rather than fixed effects to account for the subjective nature of the selection of categories in Likert items. The parameters of the new model can be estimated with the JAGS (Just Another Gibbs…
Descriptors: Computer Assisted Testing, Adaptive Testing, Models, Bayesian Statistics
Culpepper, Steven Andrew – Applied Psychological Measurement, 2012
Measurement error significantly biases interaction effects and distorts researchers' inferences regarding interactive hypotheses. This article focuses on the single-indicator case and shows how to accurately estimate group slope differences by disattenuating interaction effects with errors-in-variables (EIV) regression. New analytic findings were…
Descriptors: Evidence, Test Length, Interaction, Regression (Statistics)
Furlow, Carolyn F.; Ross, Terris Raiford; Gagne, Phill – Applied Psychological Measurement, 2009
Douglas, Roussos, and Stout introduced the concept of differential bundle functioning (DBF) for identifying the underlying causes of differential item functioning (DIF). In this study, reference group was simulated to have higher mean ability than the focal group on a nuisance dimension, resulting in DIF for each of the multidimensional items…
Descriptors: Test Bias, Test Items, Reference Groups, Simulation
de la Torre, Jimmy; Song, Hao – Applied Psychological Measurement, 2009
Assessments consisting of different domains (e.g., content areas, objectives) are typically multidimensional in nature but are commonly assumed to be unidimensional for estimation purposes. The different domains of these assessments are further treated as multi-unidimensional tests for the purpose of obtaining diagnostic information. However, when…
Descriptors: Ability, Tests, Item Response Theory, Data Analysis
Finch, Holmes – Applied Psychological Measurement, 2010
The accuracy of item parameter estimates in the multidimensional item response theory (MIRT) model context is one that has not been researched in great detail. This study examines the ability of two confirmatory factor analysis models specifically for dichotomous data to properly estimate item parameters using common formulae for converting factor…
Descriptors: Item Response Theory, Computation, Factor Analysis, Models
Woods, Carol M. – Applied Psychological Measurement, 2007
Ramsay curve item response theory (RC-IRT) was recently developed to detect and correct for nonnormal latent variables when unidimensional IRT models are fitted to data using maximum marginal likelihood estimation. The purpose of this research is to evaluate the performance of RC-IRT for Likert-type item responses with varying test lengths, sample…
Descriptors: Test Length, Item Response Theory, Sample Size, Comparative Analysis

Sanders, Piet F.; Verschoor, Alfred J. – Applied Psychological Measurement, 1998
Presents minimization and maximization models for parallel test construction under constraints. The minimization model constructs weakly and strongly parallel tests of minimum length, while the maximization model constructs weakly and strongly parallel tests with maximum test reliability. (Author/SLD)
Descriptors: Algorithms, Models, Reliability, Test Construction
de la Torre, Jimmy; Stark, Stephen; Chernyshenko, Oleksandr S. – Applied Psychological Measurement, 2006
The authors present a Markov Chain Monte Carlo (MCMC) parameter estimation procedure for the generalized graded unfolding model (GGUM) and compare it to the marginal maximum likelihood (MML) approach implemented in the GGUM2000 computer program, using simulated and real personality data. In the simulation study, test length, number of response…
Descriptors: Computation, Monte Carlo Methods, Markov Processes, Item Response Theory
Finch, Holmes – Applied Psychological Measurement, 2005
This study compares the ability of the multiple indicators, multiple causes (MIMIC) confirmatory factor analysis model to correctly identify cases of differential item functioning (DIF) with more established methods. Although the MIMIC model might have application in identifying DIF for multiple grouping variables, there has been little…
Descriptors: Identification, Factor Analysis, Test Bias, Models