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.

Misleading response behavior is expected in medical settings where incriminating behavior is negatively related to the recovery from a disease. In the present study, lung patients feel social and professional pressure concerning smoking and experience questions about smoking behavior as sensitive and tend to conceal embarrassing or threatening information. The randomized item-response survey method is expected to improve the accuracy of self-reports as individual item responses are masked and only randomized item responses are observed. We explored the validation of the randomized item-response technique in a unique experimental study. Therefore, we administered a new multi-item measure assessing smoking behavior by using a treatment-control design (randomized response (RR) or direct questioning). After the questionnaire, we administered a breath test by using a carbon monoxide (CO) monitor to determine the smoking status of the patient. We used the response data to measure the individual smoking behavior by using a mixture item-response model. It is shown that the detected smokers scored significantly higher in the RR condition compared with the directly questioned condition. We proposed a Bayesian latent variable framework to evaluate the diagnostic test accuracy of the questionnaire using the randomized-response technique, which is based on the posterior densities of the subject's smoking behavior scores together with the breath test measurements. For different diagnostic test thresholds, we obtained moderate posterior mean estimates of sensitivity and specificity by observing a limited number of discrete randomized item responses.

Fox, Klein Entink, Avetisyan BJMSP (March, 2013).This document provides a step by step analysis of the CAPS and AEQ data using the Compensatory and Noncompensatory Multidimensional Randomized Item Response Models. The summarized output and description of the model is in Fox et al (2013, BJMSP). Here, a more detailed description is given of the function calls and the output. This document is not meant to serve as program manual, since it only provides information about the specific model analysis as described in the paper. REAL Data Study:The R-work directory with stored data objects is called "DataObjects.Rdata" load("DataObjects.Rdata") #load data setThe response matrix Y is defined to be N (793) times K (17) where the response options are integers from 1-5. The data object X contains a column with all ones (intercept), an indicator variable that equals zero when subject responded using the randomizing device (RRT = 0) and one when responded directly. The third column of X is an indicator that equals one when subject is female and zero when male. When there are no explanatory variables, an intercept still needs to be defined through a column of ones.An RR object is made that specifies which response is answered via the randomized response procedure (RR=1) and which one via direct questioning (RR=0).

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