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Please use this identifier to cite or link to this item: http://hdl.handle.net/11299/119466

Title: Conditional covariance-based nonparametric multidimensionality assessment
Authors: Stout, William
Habing, Brian
Douglas, Jeff
Kim, Hae Rim
Roussos, Louis
Zhang, Jinming
Issue Date: 1996
Citation: Stout, William, Habing, Brian, Douglas, Jeff & Kim, Hae Rim. (1996). Conditional covariance-based nonparametric multidimensionality assessment. Applied Psychological Measurement, 20, 331-354. doi:10.1177/014662169602000403
Abstract: According to the weak local independence approach to defining dimensionality, the fundamental quantities for determining a test’s dimensional structure are the covariances of item-pair responses conditioned on examinee trait level. This paper describes three dimensionality assessment procedures-HCA/CCPROX, DIMTEST, and DETECT-that use estimates of these conditional covariances. All three procedures are nonparametric ; that is, they do not depend on the functional form of the item response functions. These procedures are applied to a dimensionality study of the LSAT, which illustrates the capacity of the approaches to assess the lack of unidimensionality, identify groups of items manifesting approximate simple structure, determine the number of dominant dimensions, and measure the amount of multidimensionality. Index terms: approximate simple structure, conditional covariance, DETECT, dimensionality, DIMTEST, HCA/CCPROX, hierarchical cluster analysis, IRT, LSAT, local independence, multidimensionality, simple structure.
URI: http://purl.umn.edu/119466
Appears in Collections:Volume 20, 1996

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