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Ferrari, Pier Alda; Barbiero, Alessandro – Multivariate Behavioral Research, 2012
The increasing use of ordinal variables in different fields has led to the introduction of new statistical methods for their analysis. The performance of these methods needs to be investigated under a number of experimental conditions. Procedures to simulate from ordinal variables are then required. In this article, we deal with simulation from…
Descriptors: Data, Statistical Analysis, Sampling, Simulation
Cafri, Guy; Kromrey, Jeffrey D.; Brannick, Michael T. – Multivariate Behavioral Research, 2010
This article uses meta-analyses published in "Psychological Bulletin" from 1995 to 2005 to describe meta-analyses in psychology, including examination of statistical power, Type I errors resulting from multiple comparisons, and model choice. Retrospective power estimates indicated that univariate categorical and continuous moderators, individual…
Descriptors: Periodicals, Effect Size, Sampling, Psychology
Steyn, H. S., Jr.; Ellis, S. M. – Multivariate Behavioral Research, 2009
When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…
Descriptors: Effect Size, Multivariate Analysis, Computation, Monte Carlo Methods

Hall, Charles E. – Multivariate Behavioral Research, 1974
Descriptors: Analysis of Variance, Comparative Analysis, Correlation, History

MacCallum, Robert C.; And Others – Multivariate Behavioral Research, 1994
Alternative strategies for two-sample cross-validation of covariance structure models are described and investigated. Results of an empirical sampling study show that for tighter strategies simpler models are preferred in smaller samples, but when cross-validation is employed, a more complex model is supported even for small samples. (SLD)
Descriptors: Comparative Analysis, Evaluation Methods, Models, Research Methodology

Buss, Allan R. – Multivariate Behavioral Research, 1975
The procedures involve a planned data gathering strategy consisting of at least two different groups, each receiving two different test batteries. A combination of Tucker's interbattery technique and congruence measures was the recommended strategy. Limitations of the concept of factor invariance are briefly discussed. (Author/BJG)
Descriptors: Comparative Analysis, Data Collection, Factor Analysis, Measurement Techniques

Feild, Hubert S.; And Others – Multivariate Behavioral Research, 1975
Empirically determines if two approaches, i.e., individual predictor information versus group information, would yield different predictive results, and since the second approach involved the additional expense of grouping whether the prediction of criterion measures by individual data could be enhanced by the addition of group data. (Author/RC)
Descriptors: College Students, Comparative Analysis, Correlation, Homogeneous Grouping

Dudzinski, M. L.; And Others – Multivariate Behavioral Research, 1975
Descriptors: Comparative Analysis, Correlation, Factor Analysis, Homogeneous Grouping

Hakstian, Ralph A.; Skakun, Ernest N. – Multivariate Behavioral Research, 1976
Populations of factorially simple and complex data were generated with first the oblique and orthogonal factor models, and then solutions based on special cases of the general orthomax criterion were compared on the basis of these characteristics. The results are discussed and implications noted. (DEP)
Descriptors: Comparative Analysis, Factor Analysis, Mathematical Models, Matrices