Adaptive classification testing (ACT) is a variation of computerized adaptive testing (CAT) that is developed to efficiently classify examinees into multiple groups based on predetermined cutoffs. In multidimensional multiclassification (i.e., more than two categories exist along each dimension), grid classification is proposed to classify each examinee into one of the grids encircled by cutoffs (lines/surfaces) along different dimensions so as to provide clearer information regarding an examinee’s relative standing along each dimension and facilitate subsequent treatment and intervention. In this article, the sequential probability ratio test (SPRT) and confidence interval method were implemented in the grid multiclassification ACT. In addition, two new termination criteria, the grid classification generalized likelihood ratio (GGLR) and simplified grid classification generalized likelihood ratio were proposed for grid multiclassification ACT. Simulation studies, using a simulated item bank, and a real item bank with polytomous multidimensional items, show that grid multiclassification ACT is more efficient than classification based on measurement CAT that focuses on trait estimate precision. In the context of a high-quality bank, GGLR was found to most efficiently terminate the grid multiclassification ACT and classify examinees.
|Original language||English (US)|
|Number of pages||20|
|Journal||Applied Psychological Measurement|
|State||Published - Oct 2022|
Bibliographical noteFunding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by Institute of Education Sciences under Award Number R305D200015 to the University of Washington and the Eunice Kennedy Shriver National Institutes of Child Health and Human Development of the National Institutes of Health under Award Number R01HD079439 to the Mayo Clinic in Rochester Minnesota through subcontracts to the University of Minnesota and the University of Washington. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
© The Author(s) 2022.
- adaptive classification testing
- computerized adaptive testing
- grid multiclassification
- polytomous items
- sequential probability ratio test, termination criteria
PubMed: MeSH publication types
- Journal Article