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A Refinement on DETECT for Polytomous Test Data

Kim, Hae-Rim

  • Published : 2006.12.31

Abstract

A multidimensionality detecting procedure DETECT, based on conditional covariances between items, is extended and refined to deal with polytomous item data as well as binary one. A large body of simulation study shows extraordinary performance of DETECT in both enumerating degrees of multidimensionality in a test and discovering dimensionally distinctive item clusters. Real data study also provides very meaningful results, making DETECT a strong dimensionality assessment tool for the test data analysis.

Keywords

DETECT;polytomous;test dimensionality;dimensionally distinctive

References

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