This study investigates the effect of multidimensionality on extraction of latent classes in mixture Rasch models. In this study, two-dimensional data were generated under varying conditions. The two-dimensional data sets were analyzed with one- to five-class mixture Rasch models. Results of the simulation study indicate the mixture Rasch model…

This study examines model selection indices for use with dichotomous mixture item response theory (IRT) models. Five indices are considered: Akaike's information coefficient (AIC), Bayesian information coefficient (BIC), deviance information coefficient (DIC), pseudo-Bayes factor (PsBF), and posterior predictive model checks (PPMC). The five…

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)

Type I error rates of the likelihood ratio test for the detection of differential item functioning (DIF) in the partial credit model were investigated using simulated data. The partial credit model with four ordered performance levels was used to generate data sets of a 30-item test for samples of 300 and 1,000 simulated examinees. Three different…

Conducted a Monte Carlo study to compare various approaches to detecting differential item functioning (DIF) under a conceptualization of DIF that recognizes that observed data are a mixture of data from multiple latent populations or classes. Demonstrated the usefulness of the approach. (SLD)

Studied results from five linking methods under the graded-response model using simulated data. Results show that differences in the linking coefficients are small. The five methods yielded similar results for longer common-item links with large sample sizes and when the distribution of item-location parameters matched the underlying trait…

Compared three methods for developing a common metric under item response theory through simulation. For smaller numbers of common items, linking using the characteristic curve method yielded smaller root mean square differences for both item discrimination and difficulty parameters. For larger numbers of common items, the three methods were…

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…

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…

Studies of differential item functioning (DIF) under item response theory require that item parameter estimates be placed on the same metric before comparisons can be made. Evidence that methods for linking metrics may be influenced by the presence of differentially functioning items has been inconsistent. The effects of three methods for linking…

Hierarchical Bayes procedures were compared for estimating item and ability parameters in item response theory. Simulated data sets from the two-parameter logistic model were analyzed using three different hierarchical Bayes procedures: (1) the joint Bayesian with known hyperparameters (JB1); (2) the joint Bayesian with information hyperpriors…