PurposeTo investigate whether the Stanford Health Assessment Questionnaire Disability Index (HAQ-DI) can serve as a generic instrument for measuring disability across different rheumatic diseases and to propose a scoring method based on item response theory (IRT) modeling to support this goal.MethodsThe HAQ-DI was administered to a cross-sectional sample of patients with confirmed rheumatoid arthritis (n = 619), osteoarthritis (n = 125), or gout (n = 102). The results were analyzed using the generalized partial credit model as an IRT model.ResultsIt was found that 4 out of 8 item categories of the HAQ-DI displayed substantial differential item functioning (DIF) over the three diseases. Further, it was shown that this DIF could be modeled using an IRT model with disease-specific item parameters, which produces measures that are comparable for the three diseases.ConclusionAlthough the HAQ-DI partially functioned differently in the three disease groups, the measurement regarding the disability level of the patients can be made comparable using IRT methods.

A classification method is presented for adaptive classification testing with a multidimensional item response theory (IRT) model in which items are intended to measure multiple traits, that is, within-dimensionality. The reference composite is used with the sequential probability ratio test (SPRT) to make decisions and decide whether testing can be stopped before reaching the maximum test length. Item-selection methods are provided that maximize the determinant of the information matrix at the cutoff point or at the projected ability estimate. A simulation study illustrates the efficiency and effectiveness of the classification method. Simulations were run with the new item-selection methods, random item selection, and maximization of the determinant of the information matrix at the ability estimate. The study also showed that the SPRT with multidimensional IRT has the same characteristics as the SPRT with unidimensional IRT and results in more accurate classifications than the latter when used for multidimensional data.

Computerized classification tests classify examinees into two or more levels while maximizing accuracy and minimizing test length. The majority of currently available item selection methods maximize information at one point on the ability scale, but in a test with multiple cutting points selection methods could take all these points simultaneously into account. If for each cutting point one objective is specified, the objectives can be combined into one optimization function using multiple objective approaches. Simulation studies were used to compare the efficiency and accuracy of eight selection methods in a test based on the sequential probability ratio test. Small differences were found in accuracy and efficiency between different methods depending on the item pool and settings of the classification method. The size of the indifference region had little influence on accuracy but considerable influence on efficiency. Content and exposure control had little influence on accuracy and efficiency.

Multidimensional computerized classification testing can be used when classification decisions are required for constructs that have a multidimensional structure. Here, two methods for making those decisions are included for two types of multidimensionality. In the case of between-item multidimensionality, each item is intended to measure just one dimension. In the case of within-item multidimensionality, items are intended to measure multiple or all dimensions. Wald's (1947) sequential probability ratio test and Kingsbury and Weiss (1979) confidence interval method can be applied to multidimensional classification testing. Three methods are included for selecting the items: random item selection, maximization at the current ability estimate, and the weighting method. The last method maximizes information based on a combination of the cutoff points weighted by their distance to the ability estimate. Two examples illustrate the use of the classification and item selection methods. 14.1 Introduction Computerized classification tests, like computerized adaptive tests, adapt some test elements to the student. Test length and/or item selection is tailored during test administration. The goal of a computerized classification test is to classify the examinee into one of two levels (e.g., master/nonmaster) or into one of multiple levels (e.g., basic/proficient/advanced). Classification methods stop testing when enough confi

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