Item response theory item parameters can be estimated using data from a common-item equating design either separately for each form or concurrently across forms. This paper reports the results of a simulation study of separate versus concurrent item parameter estimation. Using simulated data from a test with 60 dichotomous items, four factors were considered: (a) estimation program (MULTILOG versus BILOG-MG), (b) sample size per form (3,000 versus 1,000), (c) number of common items (20 versus 10), and (d) equivalent versus nonequivalent groups taking the two forms (no mean difference versus a mean difference of 1 SD). In addition, four methods of item parameter scaling were used in the separate estimation condition: two item characteristic curve methods (Stocking-Lord and Haebara) and two moment methods (Mean/Mean and Mean/Sigma). Concurrent estimation generally resulted in lower error than separate estimation, although not universally so. The results suggest that one factor accounting for the lower error when using concurrent estimation may be that the parameter estimates for the common item parameters are based on larger samples. It is argued that the results of this study, together with other research on this topic, are not sufficient to recommend completely avoiding separate estimation in favor of concurrent estimation.

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This article proposes an item response model that incorporates response time. A parameter estimation procedure using the EM algorithm is developed. The procedure is evaluated with both real and simulated test data. The results suggest that the estimation procedure works well in estimating model parameters. By using response time data, estimation of person ability parameters can be improved. Potential applications of this model are discussed. Directions for further study are suggested.

The purpose of this study was to compare and evaluate five on‐line pretest item‐calibration/scaling methods in computerized adaptive testing (CAT): marginal maximum likelihood estimate with one EM cycle (OEM), marginal maximum likelihood estimate with multiple EM cycles (MEM), Stocking's Method A, Stocking's Method B, and BILOG/Prior. The five methods were evaluated in terms of item‐parameter recovery, using three different sample sizes (300, 1000 and 3000). The MEM method appeared to be the best choice among these, because it produced the smallest parameter‐estimation errors for all sample size conditions. MEM and OEM are mathematically similar, although the OEM method produced larger errors. MEM also was preferable to OEM, unless the amount of time involved in iterative computation is a concern. Stocking's Method B also worked very well, but it required anchor items that either would increase test lengths or require larger sample sizes depending on test administration design. Until more appropriate ways of handling sparse data are devised, the BILOG/Prior method may not be a reasonable choice for small sample sizes. Stocking's Method A had the largest weighted total error, as well as a theoretical weakness (i.e., treating estimated ability as true ability); thus, there appeared to be little reason to use it.

This article describes procedures for estimating various indices of classification consistency and accuracy for multiple category classifications using data from a single test administration. The estimates of the classification consistency and accuracy indices are compared under three different psychometric models: the two-parameter beta binomial, four-parameter beta binomial, and three-parameter logistic IRT (item response theory) models. Using real data sets, the estimation procedures are illustrated, and the characteristics of the estimated classification indices are examined. This article also examines the behavior of the estimated classification indices as a function of the latent variable. All three components of the models (i.e., the estimated true score distributions, fitted observed score distributions, and estimated conditional error variances) appear to have considerable influence on the magnitudes of the estimated classification indices. Choosing a model in practice should be based on various considerations including the degree of model fit to the data, suitability of the model assumptions, and the computational feasibility.