When tests are administered underhed time constraints, test performunces can be affected by speededness. Among other consequences, speededness can result in inaccurate parameter estimates in item response theory (IRT) models, especially for items located near the end of tests (Oshima, 1994). This article presents an IRT strategy for reducing contamination in item difficulty estimates due to speededness. Ordinal constraints are applied to a mixture Rasch model (Rost, 1990) so as to distinguish two latent classes of examinees: (a) a "speeded" class, comprised of examinees that had insufficient time to adequately answer end-of-test items, and (b) a "nonspeeded" class, comprised of examinees that had sufficient time to answer all items. The parameter estimates obtained for end-of-test items in the nonspeeded class are shown to more accurately approximate their difficulties when the items are administered at earlier locations on a different form of the test. A mixture model can also be used to estimate the class memberships of individual examinees. In this way, it can be determined whether membership in the speeded class is associated with other student characteristics. Results are reported for gender and ethnicity.

When examinees copy answers to test questions from other examinees, the validity of the test is compromised. Most available statistical procedures for detecting copying were developed out of classical test theory (CrT); hence, they suffer from sampledependent score and item statistics, and biased estimates of the expected number of answer matches between a pair of examinees. Item response theory (IRT) based procedures alleviate these problems; however, because they fail to compare the similarity of responses between neighboring examinees, they have relatively poor power for detecting copiers. A new IRT-based test statistic, wo, was compared with the best CUT-based index g2 under various copying conditions, amounts of copying, test lengths, and sample sizes. w consistently held the Type I error rate at or below the nominal level; g2 yielded substantially inflated Type I error rates. The power of w varied as a function of both test length and the percentage of items copied. w demonstrated good power to detect copiers, provided that at least 20% of the items were copied on an 80-item test and at least 30% were copied on a 40-item test. Based on these results, with regard to both Tbype I error rate and power, c appears to be more useful than g2 as a copying index.

Test tampering, especially on tests for educational accountability, is an unfortunate reality, necessitating that the state (or its testing vendor) perform data forensic analyses, such as erasure analyses, to look for signs of possible malfeasance. Few statistical approaches exist for detecting fraudulent erasures, and those that do largely do not lend themselves to making probabilistic statements about the likelihood of the observations. In this article, a new erasure detection index, , is developed, which uses item response theory to compare the number of observed wrong-to-right erasures to the number expected due to chance, conditional on the examinee's ability-level and number of erased items. A simulation study is presented to evaluate the Type I error rate and power of under various types of fraudulent and benign erasures. Results show that with a correction for continuity yields Type I error rates that are less than or equal to nominal levels for every condition studied, and has high power to detect even small amounts of tampering among the students for whom tampering is most likely.

Type I error rates for the likelihood ratio test for detecting differential item functioning (DIF) were investigated using monte carlo simulations. Two-and three-parameter item response theory (IRT) models were used to generate 100 datasets of a 50-item test for samples of 250 and 1,000 simulated examinees for each IRT model. Item parameters were estimated by marginal maximum likelihood for three IRT models: the three-parameter model, the three-parameter model with a fixed guessing parameter, and the two-parameter model. All DIF comparisons were simulated by randomly pairing two samples from each sample size and IRT model condition so that, for each sample size and IRT model condition, there were 50 pairs of reference and focal groups. Type I error rates for the two-parameter model were within theoretically expected values at each of the α levels considered. Type I error rates for the three-parameter and three-parameter model with a fixed guessing parameter, however, were different from the theoretically expected values at the α levels considered. Index terms: bias, differential item functioning, item bias, item response theory, likelihood ratio test for DIF. An item is said to be functioning differentially when the probability of a correct response is different for examinees at the same trait level but from different groups (Pine, 1977). Because the presence of such items on a test is a threat to validity and may seriously interfere with efforts to equate tests, they must be removed from consideration. Thissen,

A mixture item response model is proposed for investigating individual differences in the selection of response categories in multiple-choice items. The model accounts for local dependence among response categories by assuming that examinees belong to discrete latent classes that have different propensities towards those responses. Varying response category propensities are captured by allowing the category intercept parameters in a nominal response model ( Bock, 1972 ) to assume different values across classes. A Markov Chain Monte Carlo algorithm for the estimation of model parameters and classification of examinees is described. A real-data example illustrates how the model can be used to distinguish examinees that are disproportionately attracted to different types of distractors in a test of English usage. A simulation study evaluates item parameter recovery and classification accuracy in a hypothetical multiple-choice test designed to be diagnostic. Implications for test construction and the use of multiple-choice tests to perform cognitive diagnoses of item response patterns are discussed.