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Direct linkSen, Sedat; Cohen, Allan S. – Educational and Psychological Measurement, 2023
The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL. Manipulated factors in the simulation included the sample size (11 different sample sizes from 100 to 5000), test…
Descriptors: Sample Size, Item Response Theory, Accuracy, Classification
Jang, Yoonsun; Cohen, Allan S. – Educational and Psychological Measurement, 2020
A nonconverged Markov chain can potentially lead to invalid inferences about model parameters. The purpose of this study was to assess the effect of a nonconverged Markov chain on the estimation of parameters for mixture item response theory models using a Markov chain Monte Carlo algorithm. A simulation study was conducted to investigate the…
Descriptors: Markov Processes, Item Response Theory, Accuracy, Inferences
Choi, Youn-Jeng; Alexeev, Natalia; Cohen, Allan S. – International Journal of Testing, 2015
The purpose of this study was to explore what may be contributing to differences in performance in mathematics on the Trends in International Mathematics and Science Study 2007. This was done by using a mixture item response theory modeling approach to first detect latent classes in the data and then to examine differences in performance on items…
Descriptors: Test Bias, Mathematics Achievement, Mathematics Tests, Item Response Theory
Peer reviewedCohen, Allan S.; Kane, Michael T.; Kim, Seock-Ho – Applied Psychological Measurement, 2001
Discusses reasons why increasing the number of replications in Monte Carlo simulation studies is not necessary for satisfactory levels of precision and offers guidelines in the context of error tolerance analysis for determining how much precision is needed. (SLD)
Descriptors: Monte Carlo Methods, Simulation
Peer reviewedWollack, James A.; Bolt, Daniel M.; Cohen, Allan S.; Lee, Young-Sun – Applied Psychological Measurement, 2002
Compared the quality of item parameter estimates for marginal maximum likelihood (MML) and Markov Chain Monte Carlo (MCMC) with the nominal response model using simulation. The quality of item parameter recovery was nearly identical for MML and MCMC, and both methods tended to produce good estimates. (SLD)
Descriptors: Estimation (Mathematics), Markov Processes, Monte Carlo Methods, Simulation
Download full textKim, Seock-Ho; Cohen, Allan S. – 1999
The accuracy of Gibbs sampling, a Markov chain Monte Carlo procedure, was considered for estimation of item and ability parameters under the two-parameter logistic model. Memory test data were analyzed to illustrate the Gibbs sampling procedure. Simulated data sets were analyzed using Gibbs sampling and the marginal Bayesian method. The marginal…
Descriptors: Bayesian Statistics, Estimation (Mathematics), Item Response Theory, Markov Processes
Kim, Seock-Ho; Cohen, Allan S. – 1998
The accuracy of the Markov Chain Monte Carlo (MCMC) procedure Gibbs sampling was considered for estimation of item parameters of the two-parameter logistic model. Data for the Law School Admission Test (LSAT) Section 6 were analyzed to illustrate the MCMC procedure. In addition, simulated data sets were analyzed using the MCMC, marginal Bayesian…
Descriptors: Bayesian Statistics, Estimation (Mathematics), Higher Education, Markov Processes
Peer reviewedKim, Seock-Ho; Cohen, Allan S. – Applied Psychological Measurement, 1998
Investigated Type I error rates of the likelihood-ratio test for the detection of differential item functioning (DIF) using Monte Carlo simulations under the graded-response model. Type I error rates were within theoretically expected values for all six combinations of sample sizes and ability-matching conditions at each of the nominal alpha…
Descriptors: Ability, Item Bias, Item Response Theory, Monte Carlo Methods
Peer reviewedCohen, Allan S.; And Others – Applied Psychological Measurement, 1996
Type I error rates for the likelihood ratio test for detecting differential item functioning (DIF) were investigated using Monte Carlo simulations. Type I error rates for the two-parameter model were within theoretically expected values at each alpha level, but those for the three-parameter model were not. (SLD)
Descriptors: Identification, Item Bias, Item Response Theory, Maximum Likelihood Statistics
Kim, Seock-Ho; Cohen, Allan S. – 1997
Type I error rates of the likelihood ratio test for the detection of differential item functioning (DIF) were investigated using Monte Carlo simulations. The graded response model with five ordered categories was used to generate data sets of a 30-item test for samples of 300 and 1,000 simulated examinees. All DIF comparisons were simulated by…
Descriptors: Ability, Classification, Computer Simulation, Estimation (Mathematics)
