Using New Proximity Measures With Hierarchical Cluster Analysis to Detect Multidimensionality

A new approach for partitioning test items into dimensionally distinct item clusters is introduced. The core of the approach is a new item-pair conditional-covariance-based proximity measure that can be used with hierarchical cluster analysis. An extensive simulation study designed to test the limits of the approach indicates that when approximate simple structure holds, the procedure can correctly partition the test into dimensionally homogeneous item clusters even for very high correlations between the latent dimensions. In particular, the procedure can correctly classify (on average) over 90% of the items for correlations as high as .9. The cooperative role that the procedure can play when used in conjunction with other dimensionality assessment procedures is discussed.