USE OF GENERALIZED PERSON-FIT INDEXES, ZETAS FOR STATISTICAL PATTERN-CLASSIFICATION

Person-fit indexes have often been used for detecting aberrant respons e patterns resulting from unusual behavior such as cheating. Because t he focus of these indexes is on an individual's performance on a test rather than on sample statistics, they should be able to be used for c ognitive diagnosis. However, cognitive theories show that a number of different sources of misconception affect test performance, and some m isconceptions can be seen in many classrooms. Therefore, application o f person-fit statistics to cognitive diagnosis requires a special cons ideration, whereby we have to detect ''normal'' and ''usual'' response patterns resulting from several sources of misconception that are fre quently observed among students. This study shows a solution for this problem by introducing an extension of Tatsuoka's (1985) zeta index-ge neralized zeta s-and discusses their statistical properties in the con text of cognitive diagnosis.