Detecting and Correcting Adaptive and Conventional Test Compromise
Small Business Information
Adams (Currently Algorithm Design & Measurement)
905 Shurts St, Urbana, IL, 61801
AbstractProject objectives briefly described: i. Improve accuracy of measurement for cheating examinees by using a scoring method that takes account of the credibility of the examinee's patterm of wrong answers. ii. Provide an adjunct to on-line calibration that would assist in timing the replacement of items. iii. Identify test sites and recruiters that are associated with high rates of item compromise. iv. Improve accuary of measurement of normal examinees by utilizing ancillary information such as frequency of item usage and likelihood of item compromise in the examinee's testing site. These objectives are to be achieved by modeling the probability that an item has been compromised and the probability that a particular examinee has previewed one or more items. The parameters of the models are to be estimated from both group and individual data. Accuracy of estimation is to be improved by explicitly incorporating information about test compromise in the calculation of the individual examinee's posterior ability distribution. Statistical tests are to be used to identify compromised items. Statistical tests and estimated distributions of aberrance are to be used to identify test sites and recruiters involved in test compromise.
* information listed above is at the time of submission.