|Adaptive Testing of the Number Series Test Using Standard Approaches and a New Decision Tree Analysis Approach
|Year of Publication
|McArdle, JJ, McArdle, JJ, Ritschard, G
|Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences
|New York, NY
|Computerized Adaptive Testing, Decision Tree Analysis
In this chapter the author attempts to demonstrate the utility of a Decision Tree Analysis (DTA) approach to Computerized Adaptive Testing (CAT). The basic psychometric premise used here is that if the overall (or total) score for any individual comes from a full set of items (I ), the behavior on a smaller number of items (i < I ) can be used to mimic the overall tests score with lower but substantial accuracy. If so, then these speciﬁc items may be administered instead of the complete set and this could save valuable administration time. In essence, once the test is constructed, the DTA approach is not theory bound and neither is the CAT approach. The reduction in accuracy could always be compared to the time gained by not having to administer irrelevant items. This principle is illustrated with data from a speciﬁc test, 47 items from the published Number Series (NS) test (from the Cognition and Aging in the USA (CogUSA) experiment), where all individuals (N > 1,200) were effectively administered all 47 items. Standard CAT strategies are compared to the DTA approach and we conclude that the DTA is much more accurate with a scale reliability of ρ2 = 0.85 with only 4-7 items administered. Other costs versus beneﬁts type experiments are suggested.
|User Guide Notes