Item preknowledge describes a situation in which a group of examinees (called ) have had access to some items (called) from an administered test prior to the exam. Item preknowledge negatively affects both the corresponding testing program and its users (e.g., universities, companies, government organizations) because scores for aberrant examinees are invalid. In general, item preknowledge is hard to detect due to multiple unknowns: unknown groups of aberrant examinees (at unknown test centers or schools) accessing unknown subsets of items prior to the exam. Recently, multiple statistical methods were developed to detect compromised items. However, the detected subset of items (called the ) naturally has an uncertainty due to false positives and false negatives. The uncertainty increases when different groups of aberrant examinees had access to different subsets of items; thus, compromised items for one group are uncompromised for another group and vice versa. The impact of uncertainty on the performance of eight statistics (each relying on the suspicious subset) was studied. The measure of performance was based on the receiver operating characteristic curve. Computer simulations demonstrated how uncertainty combined with various independent variables (e.g., type of test, distribution of aberrant examinees) affected the performance of each statistic.
PFA-based planning may be superior to the current practice of using anatomic atlases that provide delineation of the target structure only, because it is more precise and provides a unique target point in the stereotactic space. This best stereotactic target is the point in the individualized atlas with the highest probability, meaning the highest probability of having the best target on the basis of the patients previously operated on. This best target is located in the hot STN, the size of which determines the precision of targeting. Because the size of the hot STN in comparison to the whole STN remains very small (1-2%) independent of whether or not lateral compensation is applied, target planning and execution have to be performed with high precision. The methodology presented, based on the PFA and on the functional volume, is general and can be applied to other structures and data sets. As numerous centers keep gathering large amounts of electrophysiological human and animal data, this work may facilitate opening new avenues in exploiting these data.
The Kullback-Leibler divergence (KLD) is a widely used method for measuring the fit of two distributions. In general, the distribution of the KLD is unknown. Under reasonable assumptions, common in psychometrics, the distribution of the KLD is shown to be asymptotically distributed as a scaled (non-central) chi-square with one degree of freedom or a scaled (doubly non-central) F. Applications of the KLD for detecting heterogeneous response data are discussed with particular emphasis on test security.
The development of statistical methods for detecting test collusion is a new research direction in the area of test security. Test collusion may be described as large-scale sharing of test materials, including answers to test items. Current methods of detecting test collusion are based on statistics also used in answer-copying detection. Therefore, in computerized adaptive testing (CAT) these methods lose power because the actual test varies across examinees. This article addresses that problem by introducing a new approach that works in two stages: in Stage 1, test centers with an unusual distribution of a person-fit statistic are identified via Kullback-Leibler divergence; in Stage 2, examinees from identified test centers are analyzed further using the person-fit statistic, where the critical value is computed without data from the identified test centers. The approach is extremely flexible. One can employ any existing person-fit statistic. The approach (room, class, college) and can be extended to support various relations between examinees (from the same undergraduate college, from the same test-prep center, from the same group at a social network). The suggested approach was found to be effective in CAT for detecting groups of examinees with item pre-knowledge, meaning those with access (possibly unknown to us) to one or more subsets of items prior to the exam.
can be applied to all major testing programs: paper-and-pencil testing (P&P), computer-based testing (CBT), multiplestage testing (MST), and CAT. Also, the definition of test center is not limited by the geographic locationUntil recently, most research on test security studied two classical problems:1. behavior often results in an unusual agreement between the incorrect answers of a pair of examinees, where one member of the pair is the source and the other member is the copier who copies answers from the source.
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