A general delta method is described for computing the standard error (SE) of a chain of linear equations. The general delta method derives the SEs directly from the moments of the score distributions obtained in the equating chain. Computer simulations demonstrate the method. (SLD)

The effect of scale selection on error in calibrating item and ability parameters was investigated, with particular reference to the standardized mean-squared difference (SMSD) statistic. Through simulation, three scaling methods for selecting the common scale were used to demonstrate their effects on SMSD values. (SLD)

The effects of different degrees of smoothing on results of equipercentile equating in random groups design using a postsmoothing method based on cubic splines were investigated, and a computer-based procedure was introduced for selecting a desirable degree of smoothing. Results suggest that no particular degree of smoothing was always optimal.…

The use of microworlds (MWs), or complex dynamic systems, in educational testing and personnel selection is hampered by systematic measurement errors because these new and innovative item formats are not adequately controlled for their difficulty. This empirical study introduces a way to operationalize an MW's difficulty and demonstrates the…

The nonparametric Mokken model of test data was compared with parametric models using simulated data through latent class analysis. It is demonstrated that latent class analysis provides a consistent comparison of item response models. (SLD)

How item bias indexes based on item response theory (IRT) identify bias that results from multidimensionality is demonstrated. Simulation results suggest that IRT-based bias indexes detect multidimensional items with bias but do not detect multidimensional items without bias. They also do not confound between-group differences on the primary test.…

This article proposes an item exposure control method, which is the extension of the Sympson and Hetter procedure and can provide item exposure control at both the item and test levels. Item exposure rate and test overlap rate are two indices commonly used to track item exposure in computerized adaptive tests. By considering both indices, item…

The theoretical approaches taken by the LOGIST and BILOG computer programs for estimating the parameters of the three-parameter logistic item response model, and their practical consequences, are compared. Guidelines for choosing between the programs for particular applications are offered. (SLD)

The equating of results from the PC-BILOG computer program to an underlying metric was studied through simulation when a two-parameter item response theory model was used. Results are discussed in terms of the identification problem and implications for test equating. (SLD)

A numerical approach for computing standard errors (SEs) of a linear equating is described in which first partial derivatives of equating functions needed to compute SEs are derived numerically. Numerical and analytical approaches are compared using the Tucker equating method. SEs derived numerically are found indistinguishable from SEs derived…

General guidelines are developed to assist practitioners in devising operational computerized adaptive testing systems based on the graded response model. The effects of the following major variables were examined: item pool size; stepsize used along the trait continuum until maximum likelihood estimation could be calculated; and stopping rule…

The adequacy of multidimensional trait and item parameters using multidimensional item response theory analysis was studied through simulation using different degrees of correlation between two trait dimensions and the existence of a restricted trait on the second dimension. Conditions allowing adequate recovery of trait and item parameters are…

Investigates the BILOG computer program's ability to recover known item parameters for different numbers of items, examinees, and variances of the prior distributions of discrimination parameters for the two-parameter logistic item-response theory model. For samples of at least 250 examinees and 15 items, simulation results support using BILOG.…

A probabilistic parallelogram model (the PARELLA model) is presented for the measurement of latent traits by proximity items. This unidimensional model assumes that the responses of persons to items result from proximity relations. The model is illustrated in an analysis of three empirical datasets from previous studies. (SLD)

The implications of serially correlated effects on the results of generalizability analyses are discussed. Simulated data are provided that demonstrate the biases that serially correlated effects introduce into the results. Serial correlation in measurement effects can have a marked influence on the impression of the dependability of measurement…