The unconditional--simultaneous--maximum likelihood (UML) estimation procedure for the one-parameter logistic model produces biased estimators. The UML method is inconsistent and is not a good alternative to conditional maximum likelihood method, at least with small numbers of items. The minimum Chi-square estimation procedure produces unbiased…

A simulation study by B. D. Wright and G. A. Douglas is critiqued, which indicates that the unconditional maximum likelihood method is an appropriate substitute for the theoretically superior conditional method for estimating parameters of the Rasch model. The study appears to rest on inadequate logic. (TJH)

General guidelines are developed to assist practitioners in devising operational computerized adaptive testing systems based on the graded response model. The effects of the following major variables were examined: item pool size; stepsize used along the trait continuum until maximum likelihood estimation could be calculated; and stopping rule…

Two joint maximum likelihood estimation methods (LOGIST 2B and LOGIST 5) and two marginal maximum likelihood estimation methods (BILOG and ForScore) were contrasted by measuring the difference between a simulation model and a model obtained by applying an estimation method to simulation data. Marginal estimation was generally superior. (SLD)

Simulation studies (N=4,000 simulees) examined the effect of misspecification of the latent ability distribution (theta) on the accuracy and efficiency of marginal maximum likelihood (MML) item parameter estimates and on MML statistics to test sufficiency and conditional independence. Results were compared to those of the conditional maximum…

The sensitivity of marginal maximum likelihood estimation of item and ability (theta) parameters was examined when prior ability distributions were not matched to underlying ability distributions. Thirty sets of 45-item test data were generated. Conditions affecting the accuracy of estimation are discussed. (SLD)

Combining Rasch and latent class models is presented as a way to overcome deficiencies and retain the positive features of both. An estimation algorithm is outlined, providing conditional maximum likelihood estimates of item parameters for each class. The model is illustrated with simulated data and real data (n=869 adults). (SLD)

This study examined the application of the marginal maximum likelihood-EM algorithm to the parameter estimation problems of the normal ogive and logistic polytomous response models for Likert-type items. A rating scale model, based on F. Samejima's (1969) graded response model, was developed. (TJH)

Using one simulated and two real data sets, the effects of the systematic variation of the item-selection procedure and the stepsize method on the operating characteristics of computerized adaptive testing (CAT) for instruments with polychotomously scored rating scale items were studied. The six rating scale CAT procedures used performed well.…

A probabilistic item response theory (IRT) model is developed for pair-comparison design in which the unfolding principle governing the choice process uses a discriminant process analogous to Thurstone's Law of Comparative Judgment. A simulation study demonstrates the feasibility of estimation, and two examples illustrate the implications for…

The effects of priors and amount of bias in the Bayesian approach to the estimation problem in item response models are examined using simulation studies. Different specifications of prior information have only modest effects on Bayesian estimates, which are less biased than joint maximum likelihood estimates for small samples. (TJH)