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Use of computerized adaptive testing (CAT) has increased substantially since it was first formulated in the 1970s. This paper provides an overview of CAT and introduces the contributions to this Special Issue. The elements of CAT discussed here include item selection procedures, estimation of the latent trait, item exposure, measurement precision, and item bank development. Some topics for future research are also presented.

An examinee-level (or conditional) reliability is proposed for use in both classical test theory (CTT) and item response theory (IRT). The well-known group-level reliability is shown to be the average of conditional reliabilities of examinees in a group or a population. This relationship is similar to the known relationship between the square of the conditional standard error of measurement (SEM) and the square of the group-level SEM. The proposed conditional reliability is illustrated with an empirical data set in the CTT and IRT frameworks.

A variety of methods have been developed to determine the extent to which a person's response vector fits an item response theory model. These person-fit methods are statistical methods that allow researchers to identify nonfitting response vectors. The most promising. method has been the 1, statistic, which is a standardized person-fit index. Reise & Due (1991) concluded that under the null condition (i.e., when data were simulated to fit the model) 1, performed reasonably well. The present study extended the findings of past researchers (e.g., Drasgow, Levine, & McLaughlin, 1987; Molenaar & Hoijtink, 1990; Reise and Due). Results show that 11 may not perform as expected when estimated person parameters (6) are used rather than true 0. This study also examined the influence of the pseudo-guessing parameter, the method used to identify nonfitting response vectors, and the method used to estimate 0. When 0 was better estimated, 1, was more normally distributed, and , the false positive rate for a single cut score did not characterize ze the distribution of lz. Changing the c parameter from .20 to 0.0 did not improve the normality of the 1. distribution. Index terms: appropriateness measurement, Bayesian estimation, item response theory, maximum likelihood estimation, person fit. During the past 15 years, several methods have been proposed to investigate a person's response pattern to a set of test items to determine whether that person is being accurately measured in the context of item response theory (IRT) models (e.g., Levine & Rubin, 1979; Tatsuoka & Linn, 1983; Trabin & Weiss, 1983). Known in the past as appropriateness measurement (e.g., Drasgow, Levine, & Williams, 1985; Tatsuoka, 1984), this area of research is now commonly referred to as person fit (e.g., Reise,1990;

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