In this study we compared five item selection procedures using three ability estimation methods in the context of a mixed-format adaptive test based on the generalized partial credit model. The item selection procedures used were maximum posterior weighted information, maximum expected information, maximum posterior weighted Kullback-Leibler…

Researchers in psychology are increasingly using model selection strategies to decide among competing models, rather than evaluating the fit of a given model in isolation. However, such interest in model selection outpaces an awareness that one or a few cases can have disproportionate impact on the model ranking. Though case influence on the fit…

The Akaike Information Criterion (AIC) was introduced to extend the method of maximum likelihood to the multimodel situation. Use of the AIC in factor analysis is interesting when it is viewed as the choice of a Bayesian model; thus, wider applications of AIC are possible. (Author/GDC)

Studied the maximum likelihood estimation of unknown parameters in a general LISREL-type model with mixed polytomous and continuous data through Monte Carlo simulation. Proposes a model selection procedure for obtaining good models for the underlying substantive theory and discusses the effectiveness of the proposed model. (SLD)

The case of adaptive testing under a multidimensional logistic response model is addressed. An adaptive algorithm is proposed that minimizes the (asymptotic) variance of the maximum-likelihood (ML) estimator of a linear combination of abilities of interest. The item selection criterion is a simple expression in closed form. In addition, it is…

A new method of multiple discriminant analysis allows a mixture of continuous and discrete predictors. It handles conditional, joint, or separate sampling. Subjects and criterion groups are represented as points in a multidimensional Euclidean space. Advantages of the method, deriving from Akaike Information Criterion model evaluation, are…

This study investigated the efficiency of item selection in a computerized adaptive test (CAT), where efficiency was defined in terms of the accumulated test information at an examinee's true ability level. A simulation methodology compared the efficiency of 2 item selection procedures with 5 ability estimation procedures for CATs of 5, 10, 15,…

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.…

In this study some alternative item selection criteria for adaptive testing are proposed. These criteria take into account the uncertainty of the ability estimates. A general weighted information criterion is suggested of which the usual maximum information criterion and the suggested alternative criteria are special cases. A simulation study was…

Maximum likelihood and Bayesian procedures are presented for item selection and scoring of multidimensional adaptive tests. A demonstration with simulated response data illustrates that multidimensional adaptive testing can provide equal or higher reliabilities with fewer items than are required in one-dimensional adaptive testing. (SLD)

A stochastic multidimensional unfolding (MDU) procedure is presented to represent individual differences in phased or sequential decision processes spatially. A Monte Carlo analysis demonstrates estimation proficiency and the appropriateness of the proposed model selection heuristic, and an application to capture awareness, consideration, and…