Browsing by Author "Mislevy, Robert J."
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item Adaptive EAP estimation of ability in a microcomputer environment(1982) Bock, R. Darrell; Mislevy, Robert J.Expected a posteriori (EAP) estimation of ability, based on numerical evaluation of the mean and variance of the posterior distribution, is shown to have unusually good properties for computerized adaptive testing. The calculations are not complex, precede noniteratively by simple summation of log likelihoods as items are added, and require only values of the response function obtainable from precalculated tables at a limited number of quadrature points. Simulation studies are reported showing the near equivalence of the posterior standard deviation and the standard error of measurement. When the adaptive testings terminate at a fixed posterior standard deviation criterion of .90 or better, the regression of the EAP estimator on true ability is virtually linear with slope equal to the reliability, and the measurement error homogeneous, in the range +- 2.5 standard deviations.Item A consumer's guide to LOGIST and BILOG(1989) Mislevy, Robert J.; Stocking, Martha L.Since its release in 1976, Wingersky, Barton, and Lord’s (1982) LOGIST has been the most widely used computer program for estimating the parameters of the three-parameter logistic item response model. An alternative program, Mislevy and Bock’s (1983) BILOG, has recently become available. This paper compares the approaches taken by the two programs and offers some guidelines for choosing between the two programs for particular applications. Index terms: Bayesian estimation, BILOG, IRT estimation procedures, LOGIST, marginal maximum likelihood, maximum likelihood, three-parameter logistic model estimation procedures.Item Exploiting auxiliary information about examinees in the estimation of item parameters(1987) Mislevy, Robert J.The precision of item parameter estimates can be increased by taking advantage of dependencies between the latent proficiency variable and auxiliary examinee variables such as age, courses taken, and years of schooling. Gains roughly equivalent to two to six additional item responses can be expected in typical educational and psychological applications. Empirical Bayesian computational procedures are presented and illustrated with data from the National Assessment of Educational Progress survey.Item Exploiting auxiliary information about items in the estimation of Rasch item difficulty parameters(1988) Mislevy, Robert J.Standard procedures for estimating the item parameters in IRT models make no use of auxiliary information about test items, such as their format, their content, or the skills they require for solution. This paper describes a framework for exploiting this information, thereby enhancing the precision and stability of item parameter estimates and providing diagnostic information about items’ operating characteristics. The principles are illustrated in a context for which a relatively simple approximation is available: empirical Bayesian estimation of Rasch item difficulty parameters. Index terms: Bayesian estimation, Collateral information, Empirical Bayesian estimation, Exchangeability, Hierarchical models, Item response theory, Linear logistic test model, Rasch model item parameters.