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ERIC Number: EJ784330
Record Type: Journal
Publication Date: 2008
Pages: 18
Abstractor: Author
Reference Count: 24
ISSN: ISSN-0146-6216
Conditional Covariance-Based Subtest Selection for DIMTEST
Froelich, Amy G.; Habing, Brian
Applied Psychological Measurement, v32 n2 p138-155 2008
DIMTEST is a nonparametric hypothesis-testing procedure designed to test the assumptions of a unidimensional and locally independent item response theory model. Several previous Monte Carlo studies have found that using linear factor analysis to select the assessment subtest for DIMTEST results in a moderate to severe loss of power when the exam lacks simple structure, the ability and difficulty parameter distributions differ greatly, or the underlying model is noncompensatory. A new method of selecting the assessment subtest for DIMTEST, based on the conditional covariance dimensionality programs DETECT and HCA/ CCPROX, is presented. Simulation studies show that using DIMTEST with this new selection method has either similar or significantly higher power to detect multidimensionality than using linear factor analysis for subtest selection, while maintaining Type I error rates around the nominal level. (Contains 3 figures and 9 tables.)
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A