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Conditional covariance-based nonparametric multidimensionality assessment
Stout, William; Habing, Brian; Douglas, Jeff; Kim, Hae Rim; Roussos, Louis; Zhang, Jinming (1996)
 

Title 
Conditional covariance-based nonparametric multidimensionality assessment

Issue Date
1996

Type
Article

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.

Appears in Collection(s)

Other Identifier(s)
other: doi:10.1177/014662169602000403

Suggested Citation
Stout, William; Habing, Brian; Douglas, Jeff; Kim, Hae Rim; Roussos, Louis; Zhang, Jinming. (1996). Conditional covariance-based nonparametric multidimensionality assessment. Retrieved from the University of Minnesota Digital Conservancy, http://purl.umn.edu/119466.


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