Introduces a new approach for partitioning test items into dimensionally distinct item clusters. The core of this approach is a new item-pair conditional-covariance-based proximity measure that can be used with hierarchical cluster analysis. The procedure can correctly classify, on average, over 90% of the items for correlations as high as 0.9.…

DETECT, the acronym for Dimensionality Evaluation To Enumerate Contributing Traits, is an innovative and relatively new nonparametric dimensionality assessment procedure used to identify mutually exclusive, dimensionally homogeneous clusters of items using a genetic algorithm ( Zhang & Stout, 1999). Because the clusters of items are mutually…