Many standardized tests are now administered via computer rather than paperand-pencil format. In a computer-based testing environment, it is possible to record not only the test taker's response to each question (item) but also the amount of time spent by the test taker in considering and answering each item. Response times (RTs) provide information not only about the test taker's ability and response behavior but also about item and test characteristics. This study focuses on the use of RTs to detect aberrant test-taker responses. An example of such aberrance is a correct answer with a short RT on a difficult question. Such aberrance may be displayed when a test taker or test takers have preknowledge of the items. Another example is rapid guessing, wherein the test taker displays unusually short RTs for a series of items. When rapid guessing occurs at the end of a timed test, it often indicates that the test taker has run out of time before completing the test. In this study, Bayesian tests of significance for detecting various types of aberrant RT patterns are proposed and evaluated. In a simulation study, the tests were successful in identifying aberrant response patterns. A real data example is given to illustrate the use of the proposed person-fit tests for RTs.

The assumption of conditional independence between response time and accuracy given speed and ability is commonly made in response time modelling. However, this assumption might be violated in some cases, meaning that the relationship between the response time and the response accuracy of the same item cannot be fully explained by the correlation between the overall speed and ability. We propose to explicitly model the residual dependence between time and accuracy by incorporating the effects of the residual response time on the intercept and the slope parameter of the IRT model for response accuracy. We present an empirical example of a violation of conditional independence from a low-stakes educational test and show that our new model reveals interesting phenomena about the dependence of the item properties on whether the response is relatively fast or slow. For more difficult items responding slowly is associated with a higher probability of a correct response, whereas for the easier items responding slower is associated with a lower probability of a correct response. Moreover, for many of the items slower responses were less informative for the ability because their discrimination parameters decrease with residual response time.

This study investigates the usefulness of item-score reliability as a criterion for item selection in test construction. Methods MS, λ6, and CA were investigated as item-assessment methods in item selection and compared to the corrected item-total correlation, which was used as a benchmark. An ideal ordering to add items to the test (bottom-up procedure) or omit items from the test (top-down procedure) was defined based on the population test-score reliability. The orderings the four item-assessment methods produced in samples were compared to the ideal ordering, and the degree of resemblance was expressed by means of Kendall's τ. To investigate the concordance of the orderings across 1,000 replicated samples, Kendall's W was computed for each item-assessment method. The results showed that for both the bottom-up and the top-down procedures, item-assessment method CA and the corrected item-total correlation most closely resembled the ideal ordering. Generally, all item assessment methods resembled the ideal ordering better, and concordance of the orderings was greater, for larger sample sizes, and greater variance of the item discrimination parameters.

It is becoming more feasible and common to register response times in the application of psychometric tests. Researchers thus have the opportunity to jointly model response accuracy and response time, which provides users with more relevant information. The most common choice is to use the hierarchical model (van der Linden, 2007, Psychometrika, 72, 287), which assumes conditional independence between response time and accuracy, given a person's speed and ability. However, this assumption may be violated in practice if, for example, persons vary their speed or differ in their response strategies, leading to conditional dependence between response time and accuracy and confounding measurement. We propose six nested hierarchical models for response time and accuracy that allow for conditional dependence, and discuss their relationship to existing models. Unlike existing approaches, the proposed hierarchical models allow for various forms of conditional dependence in the model and allow the effect of continuous residual response time on response accuracy to be item-specific, person-specific, or both. Estimation procedures for the models are proposed, as well as two information criteria that can be used for model selection. Parameter recovery and usefulness of the information criteria are investigated using simulation, indicating that the procedure works well and is likely to select the appropriate model. Two empirical applications are discussed to illustrate the different types of conditional dependence that may occur in practice and how these can be captured using the proposed hierarchical models.

Reliability is usually estimated for a total score, but it can also be estimated for item scores. Item-score reliability can be useful to assess the repeatability of an individual item score in a group. Three methods to estimate item-score reliability are discussed, known as method MS, method λ6, and method CA. The item-score reliability methods are compared with four well-known and widely accepted item indices, which are the item-rest correlation, the item-factor loading, the item scalability, and the item discrimination. Realistic values for item-score reliability in empirical-data sets are monitored to obtain an impression of the values to be expected in other empirical-data sets. The relation between the three item-score reliability methods and the four well-known item indices are investigated. Tentatively, a minimum value for the item-score reliability methods to be used in item analysis is recommended.