A polychotomous nominal response model-based computerized adaptive test (CAT) was simulated using data from 1,093 University of Texas students. The ability estimation of this model and its overall performance were compared with those of a dichotomous three-parameter logistic model-based CAT. Advantages and drawbacks of nominal response CAT are…

The effects of reviewing items and altering responses on the efficiency of computerized adaptive tests and resultant ability estimates of the examinees were explored for medical technology students (220 students could and 492 students could not review and alter their responses). Data do not support disallowing review. (SLD)

A model for constrained computerized adaptive testing is proposed in which the information in the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum information at the current ability estimate fixing…

A considerable amount of data on computerized adaptive testing (CAT) has been conducted using simulated data. However, most researchers would agree that simulations may not fully reflect the reality of examinee performance on a test. This study used maximum likelihood procedures to investigate the accuracy and efficiency of examinee ability…

Vocabulary fixed-item (FIT), computerized-adaptive (CAT), and self-adapted (SAT) tests were compared with 121 college students. CAT was more precise and efficient than SAT, which was more precise and efficient than FIT. SAT also yielded higher ability estimates for individuals with lower verbal self-concepts. (SLD)

Effect sizes have been increasingly emphasized in research as more researchers have recognized that: (1) all parametric analyses (t-tests, analyses of variance, etc.) are correlational; (2) effect sizes have played an important role in meta-analytic work; and (3) statistical significance testing is limited in its capacity to inform scientific…

The reliability of four branching algorithms commonly used in computer adaptive testing (CAT) was examined. These algorithms were: (1) maximum likelihood (MLE); (2) Bayesian; (3) modal Bayesian; and (4) crossover. Sixty-eight undergraduate college students were randomly assigned to one of the four conditions using the HyperCard-based CAT program,…

The issue of test-item selection in support of decision making in adaptive testing is considered. The number of items needed to make a decision is compared for two approaches: selecting items from an item pool that are most informative at the decision point or selecting items that are most informative at the examinee's ability level. The first…

This study examined performance of a simulated computerized adaptive test that was designed to help direct the development of a medical recertification examination. The item pool consisted of 229 single-best-answer items from a random sample of 3,000 examinees, calibrated using the two-parameter logistic model. Examinees' responses were known. For…

This study examined whether an expert system approach involving intelligent selection of items (EXSPRT-I) is as efficient as item response theory (IRT) based three-parameter adaptive mastery testing (AMT) when there are enough subjects to estimate the three IRT item parameters for all items in the test and when subjects in the item parameter…

Performance of 156 undergraduate and 48 graduate students on a self-adapted test (SFAT)--students choose the difficulty level of their test items--was compared with performance on a computer-adapted test (CAT). Those taking the SFAT obtained higher ability scores and reported lower posttest state anxiety than did CAT takers. (SLD)

Detecting differential item functioning (DIF) on test items constructed to favor 1 group over another was investigated on parameter estimates from 2 item response theory-based computer programs--BILOG and LOGIST--using data for 1,000 White and 1,000 Black college students. Use of prior distributions and marginal-maximum a posteriori estimation is…