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Padilla, Miguel A.; Divers, Jasmin; Newton, Matthew – Applied Psychological Measurement, 2012
Three different bootstrap methods for estimating confidence intervals (CIs) for coefficient alpha were investigated. In addition, the bootstrap methods were compared with the most promising coefficient alpha CI estimation methods reported in the literature. The CI methods were assessed through a Monte Carlo simulation utilizing conditions…
Descriptors: Intervals, Monte Carlo Methods, Computation, Sampling
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Alsawalmeh, Yousef M.; Feldt, Leonard S. – Applied Psychological Measurement, 1994
An approximate statistical test of the equality of two intraclass reliability coefficients based on the same sample of people is derived. Such a test is needed when a researcher wishes to compare the reliability of two measurement procedures, and both procedures can be applied to results from the same group. (SLD)
Descriptors: Comparative Analysis, Measurement Techniques, Reliability, Sampling
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Van Onna, Marieke J. H. – Applied Psychological Measurement, 2004
Coefficient "H" is used as an index of scalability in nonparametric item response theory (NIRT). It indicates the degree to which a set of items rank orders examinees. Theoretical sampling distributions, however, have only been derived asymptotically and only under restrictive conditions. Bootstrap methods offer an alternative possibility to…
Descriptors: Sampling, Item Response Theory, Scaling, Comparative Analysis
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MacCallum, Robert C.; And Others – Applied Psychological Measurement, 1979
Questions are raised concerning differences between traditional metric multiple regression, which assumes all variables to be measured on interval scales, and nonmetric multiple regression. The ordinal model is generally superior in fitting derivation samples but the metric technique fits better than the nonmetric in cross-validation samples.…
Descriptors: Comparative Analysis, Multiple Regression Analysis, Nonparametric Statistics, Personnel Evaluation
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Berger, Martjin P. F. – Applied Psychological Measurement, 1991
A generalized variance criterion is proposed to measure efficiency in item-response-theory (IRT) models. Heuristic arguments are given to formulate the efficiency of a design in terms of an asymptotic generalized variance criterion. Efficiencies of designs for one-, two-, and three-parameter models are compared. (SLD)
Descriptors: Comparative Analysis, Efficiency, Equations (Mathematics), Error of Measurement