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Refinements of Stout’s Procedure for Assessing Latent Trait Unidimensionality

Refinements of Stout’s Procedure for Assessing Latent Trait Unidimensionality This article provides a detailed investigation of Stout’s statistical procedure (the computer program DIMTEST) for testing the hypothesis that an essentially unidimensional latent trait model fits observed binary item response data from a psychological test. One finding was that DIMTEST may fail to perform as desired in the presence of guessing when coupled with many high-discriminating items. A revision of DIMTEST is proposed to overcome this limitation. Also, an automatic approach is devised to determine the size of the assessment subtests. Further, an adjustment is made on the estimated standard error of the statistic on which DIMTEST depends. These three refinements have led to an improved procedure that is shown in simulation studies to adhere closely to the nominal level of signficance while achieving considerably greater power. Finally, DIMTEST is validated on a selection of real data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Educational Statistics SAGE

Refinements of Stout’s Procedure for Assessing Latent Trait Unidimensionality

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References (53)

Publisher
SAGE
Copyright
Copyright © by SAGE Publications
ISSN
0362-9791
DOI
10.3102/10769986018001041
Publisher site
See Article on Publisher Site

Abstract

This article provides a detailed investigation of Stout’s statistical procedure (the computer program DIMTEST) for testing the hypothesis that an essentially unidimensional latent trait model fits observed binary item response data from a psychological test. One finding was that DIMTEST may fail to perform as desired in the presence of guessing when coupled with many high-discriminating items. A revision of DIMTEST is proposed to overcome this limitation. Also, an automatic approach is devised to determine the size of the assessment subtests. Further, an adjustment is made on the estimated standard error of the statistic on which DIMTEST depends. These three refinements have led to an improved procedure that is shown in simulation studies to adhere closely to the nominal level of signficance while achieving considerably greater power. Finally, DIMTEST is validated on a selection of real data sets.

Journal

Journal of Educational StatisticsSAGE

Published: Mar 1, 1993

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