ERIC Number: EJ741763
Record Type: Journal
Publication Date: 2006-Sep
Reference Count: 27
Formulation of the Detect Population Parameter and Evaluation of Detect Estimator Bias
Roussos, Louis A.; Ozbek, Ozlem Yesim
Journal of Educational Measurement, v43 n3 p215-243 Sep 2006
The development of the DETECT procedure marked an important advancement in nonparametric dimensionality analysis. DETECT is the first nonparametric technique to estimate the number of dimensions in a data set, estimate an effect size for multidimensionality, and identify which dimension is predominantly measured by each item. The efficacy of DETECT critically depends on accurate, minimally biased estimation of the expected conditional covariances of all the item pairs. However, the amount of bias in the DETECT estimator has been studied only in a few simulated unidimensional data sets. This is because the value of the DETECT population parameter is known to be zero for this case and has been unknown for cases when multidimensionality is present. In this article, integral formulas for the DETECT population parameter are derived for the most commonly used parametric multidimensional item response theory model, the Reckase and McKinley model. These formulas are then used to evaluate the bias in DETECT by positing a multidimensional model, simulating data from the model using a very large sample size (to eliminate random error), calculating the large-sample DETECT statistic, and finally calculating the DETECT population parameter to compare with the large-sample statistic. A wide variety of two- and three-dimensional models, including both simple structure and approximate simple structure, were investigated. The results indicated that DETECT does exhibit statistical bias in the large-sample estimation of the item-pair conditional covariances; but, for the simulated tests that had 20 or more items, the bias was small enough to result in the large-sample DETECT almost always correctly partitioning the items and the DETECT effect size estimator exhibiting negligible bias.
Descriptors: Evaluation Methods, Effect Size, Test Bias, Item Response Theory, Models, Simulation, Sample Size, Nonparametric Statistics, Statistical Bias, Construct Validity, Item Analysis, Error of Measurement
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
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