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Leite, Walter L.; Huang, I-Chan; Marcoulides, George A. – Multivariate Behavioral Research, 2008
This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and…
Descriptors: Mathematics, Measures (Individuals), Diabetes, Simulation

Waller, Niels G.; Underhill, J. Michael; Kaiser, Heather A. – Multivariate Behavioral Research, 1999
Presents a simple method for generating simulated plasmodes and artificial test clusters with user-defined shape, size, and orientation. For "J" clusters, indicator validity is defined as the squared correlation ratio between the cluster indicator and J-1 dummy variables. Illustrates the method through simulation. (SLD)
Descriptors: Cluster Analysis, Simulation, Test Construction, Validity

Millsap, Roger E. – Multivariate Behavioral Research, 1995
A theorem is presented that describes conditions under which measurement invariance is consistent with predictive invariance for the linear case. These two forms of invariance are shown to be inconsistent under realistic conditions, and the duality is illustrated with simulated data. Implications for group differences research are discussed. (SLD)
Descriptors: Ethnicity, Groups, Measurement Techniques, Paradox

Chan, Wai; And Others – Multivariate Behavioral Research, 1995
It is suggested that using an unbiased estimate of the weight matrix may eliminate the small or intermediate sample size bias of the asymptotically distribution-free (ADF) test statistic. Results of simulations show that test statistics based on the biased estimator or the unbiased estimate are highly similar. (SLD)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Matrices, Sample Size

Revelle, William – Multivariate Behavioral Research, 1979
Hierarchical cluster analysis is shown to be an effective method for forming scales from sets of items. Comparisons with factor analytic techniques suggest that hierarchical analysis is superior in some respects for scale construction. (Author/JKS)
Descriptors: Cluster Analysis, Factor Analysis, Item Analysis, Rating Scales