Conditional covariance-based nonparametric multidimensionality assessment

Thumbnail Image

View/Download File

Persistent link to this item

View Statistics

Journal Title

Journal ISSN

Volume Title


Conditional covariance-based nonparametric multidimensionality assessment

Published Date






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.



Related to



Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published 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

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 Digital Conservancy,

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.