The power of the procedure of W. Stout to detect deviations from essential unidimensionality in two-dimensional data was investigated for minor, moderate, and large deviations from unidimensionality using criteria for deviations from unidimensionality based on prior research. Test lengths of 20 and 40 items and sample sizes of 700 and 1,500 were…

The effects of variations in degree of range restriction and different subgroup sample sizes on the validity of several item bias detection procedures based on Item Response Theory (IRT) were investigated in a simulation study. The degree of range restriction for each of two subpopulations was varied by cutting the specified subpopulation ability…

A computer simulation was undertaken to determine the effects of using Huynh's single-administration estimates of the decision consistency indices for agreement and coefficient kappa, under conditions that violated the beta-binomial assumption. Included in the investigation were two unimodal score distributions that fit the model and two bimodal…

Monte Carlo methods are used to evaluate marginal maximum likelihood estimation of item parameters and maximum likelihood estimates of theta in the two-parameter logistic model for varying test lengths, sample sizes, and assumed theta distributions. Results with 100 datasets demonstrate the methods' general precision and stability. Exceptions are…

Previous work on the effects of dimensionality on parameter estimation was extended from dichotomous models to the polytomous graded response (GR) model. A multidimensional GR model was developed to generate data in one-, two-, and three-dimensions, with two- and three-dimensional conditions varying in their interdimensional associations. Test…

The results obtained from two basic types of pre-equatings of tests were compared: the item response theory (IRT) pre-equating and section pre-equating (SPE). The simulated data were generated from a modified three-parameter logistic model with a constant guessing parameter. Responses of two replication samples of 3000 examinees on two 72-item…

Because latent trait models require that large numbers of items be calibrated or that testing of the same large group be repeated, item parameter estimates are often obtained by administering separate tests to different groups and "linking" the results to construct an adequate item pool. Four issues were studied, based upon the analysis…

Effects of test length, sample size, and assumed ability distribution were investigated in a multiple replication Monte Carlo study under the 1-parameter (1P) and 2-parameter (2P) logistic graded model with five score levels. Accuracy and variability of item parameter and ability estimates were examined. Monte Carlo methods were used to evaluate…

Equating tests from different calibrations under item response theory (IRT) requires calculation of the slope and intercept of the appropriate linear transformation. Two methods have been proposed recently for equating graded response items under IRT, a test characteristic curve method and a minimum chi-square method. These two methods are…

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…

Goodness of fit of raw test score data were compared, using two latent trait models: the Rasch model and the Birnbaum three-parameter logistic model. Data were taken from various achievement tests and the Scholastic Aptitude Test (Verbal). A minimum sample size of 1,000 was required, and the minimum test length was 40 items. Results indicated that…