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Formulation of a DIMTEST Effect Size Measure (DESM) and Evaluation of the DESM Estimator Bias
Author(s) -
Seo Minhee,
Roussos Louis A.
Publication year - 2010
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.2010.00121.x
Subject(s) - estimator , statistics , mathematics , sample size determination , statistic , test statistic , econometrics , independence (probability theory) , monte carlo method , measure (data warehouse) , statistical hypothesis testing , computer science , data mining
DIMTEST is a widely used and studied method for testing the hypothesis of test unidimensionality as represented by local item independence. However, DIMTEST does not report the amount of multidimensionality that exists in data when rejecting its null. To provide more information regarding the degree to which data depart from unidimensionality, a DIMTEST‐based Effect Size Measure (DESM) was formulated. In addition to detailing the development of the DESM estimate, the current study describes the theoretical formulation of a DESM parameter. To evaluate the efficacy of the DESM estimator according to test length, sample size, and correlations between dimensions, Monte Carlo simulations were conducted. The results of the simulation study indicated that the DESM estimator converged to its parameter as test length increased, and, as desired, its expected value did not increase with sample size (unlike the DIMTEST statistic in the case of multidimensionality). Also as desired, the standard error of DESM decreased as sample size increased.