Using an Iterative Reallocation Partitioning Algorithm to Verify Test Multidimensionality

Douglas L. Steinley () and M. J. Brusco
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Douglas L. Steinley: University of Missouri
M. J. Brusco: Florida State University

Journal of Classification, 2019, vol. 36, issue 3, No 2, 397-413

Abstract: Abstract This article addresses the issue of assigning items to different test dimensions (e.g., determining which dimension an item belongs to) with cluster analysis. Previously, hierarchical methods have been used (Roussos et al. 1997); however, the findings here suggest that an iterative reallocation partitioning (IRP) algorithm provides interpretively similar solutions and statistically better solutions to the problem. More importantly, it is shown that the inherent nature of locally optimal solutions in the IRP algorithm leads to a method that aids in determining the appropriateness of performing a cluster analysis—a feature that is lacking in the standard hierarchical methods currently in the literature.

Keywords: Test theory; Cluster analysis; k-means clustering (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00357-019-09347-z

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