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IRT Models for Learning with Item-Specific Learning Parameters
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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
Comparative Analyses of MIRT Models and Software (BMIRT and flexMIRT)
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Yavuz, Guler; Hambleton, Ronald K. – Educational and Psychological Measurement, 2017
Application of MIRT modeling procedures is dependent on the quality of parameter estimates provided by the estimation software and techniques used. This study investigated model parameter recovery of two popular MIRT packages, BMIRT and flexMIRT, under some common measurement conditions. These packages were specifically selected to investigate the…
Descriptors: Item Response Theory, Models, Comparative Analysis, Computer Software
Comparing Three Estimation Methods for the Three-Parameter Logistic IRT Model
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Lamsal, Sunil – ProQuest LLC, 2015
Different estimation procedures have been developed for the unidimensional three-parameter item response theory (IRT) model. These techniques include the marginal maximum likelihood estimation, the fully Bayesian estimation using Markov chain Monte Carlo simulation techniques, and the Metropolis-Hastings Robbin-Monro estimation. With each…
Descriptors: Item Response Theory, Monte Carlo Methods, Maximum Likelihood Statistics, Markov Processes
Accuracy and Variability of Item Parameter Estimates from Marginal Maximum a Posteriori Estimation and Bayesian Inference via Gibbs Samplers
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Wu, Yi-Fang – ProQuest LLC, 2015
Item response theory (IRT) uses a family of statistical models for estimating stable characteristics of items and examinees and defining how these characteristics interact in describing item and test performance. With a focus on the three-parameter logistic IRT (Birnbaum, 1968; Lord, 1980) model, the current study examines the accuracy and…
Descriptors: Item Response Theory, Test Items, Accuracy, Computation
Marginal Maximum A Posteriori Item Parameter Estimation for the Generalized Graded Unfolding Model
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Roberts, James S.; Thompson, Vanessa M. – Applied Psychological Measurement, 2011
A marginal maximum a posteriori (MMAP) procedure was implemented to estimate item parameters in the generalized graded unfolding model (GGUM). Estimates from the MMAP method were compared with those derived from marginal maximum likelihood (MML) and Markov chain Monte Carlo (MCMC) procedures in a recovery simulation that varied sample size,…
Descriptors: Statistical Analysis, Markov Processes, Computation, Monte Carlo Methods
Markov Chain Monte Carlo Estimation of Item Parameters for the Generalized Graded Unfolding Model
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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