Equating an adaptive test to a linear test

Two new methods for the equating of an adaptive test to a linear test are presented. The methods are based on the conditional distributions of the observed scores on the two tests, given the examinee’s ability. They are motivated by the fact that conditioning on the examinee’s ability is necessary to allow for differences between observed-score distributions of examinees. The two methods were evaluated empirically against the traditional equipercentile method based on the marginal score distributions on the two tests and a method that uses the test characteristic function (TCF) of the linear test. The criterion in this study was the difference between the distribution of the equated score and the actual observed score on the linear test. The two conditional methods were unbiased and had mean-squared error in the equated scores comparable to the marginal equipercentile method and the TCF methods. The last two methods were strongly biased. It is argued that their bias is a consequence of the fact that they use a single equating transformation for an entire population of examinees and, therefore, have to compromise between the individual score distributions

Algorithmic test design using classical item parameters

Two optimalization models for the construction of tests with a maximal value of coefficient alpha are given. Both models have a linear form and can be solved by using a branch-and-bound algorithm. The first model assumes an item bank calibrated under the Rasch model and can be used, for instance, when classical test theory has to serve as an interface between the item bank system and a user not familiar with modern test theory. Maximization of alpha was obtained by inserting a special constraint in a linear programming model. The second model has wider applicability and can be used with any item bank for which estimates of the classical item parameter are available. The models can be expanded to meet practical constraints with respect to test composition. An empirical study with simulated data using two item banks of 500 items was carried out to evaluate the model assumptions. For Item Bank 1 the underlying response was the Rasch model, and for Item Bank 2 the underlying model was the three-parameter model. An appendix discusses the relation between item response theory and classical parameter values and adds the case of a multidimensional item bank. Three tables present the simulation study data

Digital simulation of chronopotentiometric and steady-state voltammetric curves at microelectrodes in the presence of a low concentration of supporting electrolyte

A simulation scheme for the calculation of theoretical chronopotentiograms at microelectrodes in solutions containing low amounts of supporting electrolyte is presented. The scheme allows computation of the changes in the concentration profiles of the substrates, products and the supporting electrolyte ions with time. The electrode potentials that are established after reaching the steady-state, together with the appropriate current intensities, can be used for constructing the steady-state voltammograms. The simulation of the mixed diffusional and migrational transport is based on the Crank-Nicolson method with an exponentially expanding time and space grids. The scheme does not impose any limitations on diffusion coefficients and it can be applied both to simple electrode reactions (one reactant-one product) and more complicated reactions under the assumption that the double-layer thickness is small in comparison to the diffusion layer. Five simple types of electrode reactions and an example of a more complicated scheme were considered. The results obtained demonstrate that the dependence of the steady-state limiting current on the support ratio (csupp.el./csubst) depends not only on the charge of the reactant and the product, but also on the diffusion coefficient ratio of the substrate and product. If the difference between diffusion coefficients is large, the predictions based on simpler theories available in literature can become invalid

Capitalization on item calibration error in computer adaptive testing

In adaptive testing, item selection is sequentially optimized during the test. Since the optimization takes place over a pool of items calibrated with estimation error, capitalization on these errors is likely to occur. How serious the consequences of this phenomenon are depends not only on the distribution of the estimation errors in the pool or the ratio of the test length to the pool size, but also on the structure of the item-selection criteria used. A simulation study demonstrated the existence of the phenomenon empirically. It also showed that its effect on the errors in the ability estimates interacts strongly with the distribution of the items in the pool

Integration of economic appraisal and health care policy in a health insurance system; The Dutch case

This article discusses the role of economic appraisal in insurance based health care systems, taking the case of the Netherlands as an example. The public health insurance system in this country is governed by the Health Insurance Executive Board, which policies are firmly based on the results of economic appraisal. Furthermore, reimbursement policies regarding pharmaceutical products are described, emphasizing again the position of information from economic appraisal in these policies. The article concludes with the identification of some trends in health policy and in the way it is supported by economic appraisal studies

Item Information in the Rasch Model

Fisher's information measure for the item difficulty parameter in the Rasch model and its marginal and conditional formulations are investigated. It is shown that expected item information in the unconditional model equals information in the marginal model, provided the assumption of sampling examinees from an ability distribution is made. For the logistic ability distribution considered in this paper, item information in the two models can be expressed in a closed form. Also, it is shown that for a random examinee expected item information in the conditional model is always less than that in the other two models, albeit the difference quickly decreases with an increase in test length. If the distribution of the item difficulties in the test deviates more and more from the ability distribution, item information in all three models takes smaller and smaller values. Results from a simulation study of tests with 5 and 20 items demonstrate these features numerically. Six tables present the results of the simulation study, and one graph illustrates item information in the marginal model