Applied Psychological Measurement

Spearman's factor-analytic model has been proposed as a unidimensional linear item response theory (IRT) model for continuous item responses. This article first proposes a reexpression of the model that leads to a form similar to that of standard IRT models for binary responses and discusses the item indices of difficulty discrimination and information. Next, the extension to the multiple-factor model is considered, and multidimensional counterparts of the item indices are proposed. The interpretation of the proposed measures and their usefulness in personality and attitude measurement are discussed and illustrated by means of two empirical examples.

An adaptive testing method is presented that controls the speededness of a test using predictions of the test takers' response times on the candidate items in the pool. Two different types of predictions are investigated: posterior predictions given the actual response times on the items already administered and posterior predictions that use the responses on these items as an additional source of information. In a simulation study with an adaptive test modeled after a test from the Armed Services Vocational Aptitude Battery, the effectiveness of the methods in removing differential speededness from the test was evaluated.

Differential item functioning (DIF) occurs when items on a test or questionnaire have different measurement properties for one group of people versus another, irrespective of group-mean differences on the construct. Methods for testing DIF require matching members of different groups on an estimate of the construct. Preferably, the estimate is based on a subset of group-invariant items called designated anchors. In this research, a quick and easy strategy for empirically selecting designated anchors is proposed and evaluated in simulations. Although the proposed rank-based approach is applicable to any method for DIF testing, this article focuses on likelihood-ratio (LR) comparisons between nested two-group item response models. The rank-based strategy frequently identified a group-invariant designated anchor set that produced more accurate LR test results than those using all other items as anchors. Group-invariant anchors were more difficult to identify as the percentage of differentially functioning items increased. Advice for practitioners is offered.

Computerized adaptive testing is subject to security problems, as the item bank content remains operative over long periods and administration time is flexible for examinees. Spreading the content of a part of the item bank could lead to an overestimation of the examinees' trait level. The most common way of reducing this risk is to impose a maximum exposure rate (r^max) that no item should exceed. Several methods have been proposed with this aim. All of these methods establish a single value of r^max throughout the test. This study presents a new method, the multiple-r^max method, that defines as many values of r^max as the number of items presented in the test. In this way, it is possible to impose a high degree of randomness in item selection at the beginning of the test, leaving the administration of items with the best psychometric properties to the moment when the trait level estimation is most accurate. The implementation of the multiple-r ^max method is described and is tested in simulated item banks and in an operative bank. Compared with a single maximum exposure method, the new method has a more balanced usage of the item bank and delays the possible distortion of trait estimation due to security problems, with either no or only slight decrements of measurement accuracy.