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Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)
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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
Simulating Multivariate Nonnormal Data Using an Iterative Algorithm
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Ruscio, John; Kaczetow, Walter – Multivariate Behavioral Research, 2008
Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate…
Descriptors: Monte Carlo Methods, Correlation, Sampling, Multivariate Analysis
Empirical Sampling Distributions of the Product Moment Correlation Coefficient When Bivariate Observations are Correlated
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Hummel, Thomas J.; Feltovich, Paul J. – Multivariate Behavioral Research, 1975
Monte Carlo methods were used to investigate the robustness of techniques used in judging the magnitude of a sample correlation coefficient when observations are correlated. Empirical distributions of r, t, and Fisher's z were generated. A technique for controlling error rates in certain situations is suggested. (Author/BJG)
Descriptors: Computer Science, Correlation, Error Patterns, Monte Carlo Methods
A Study of the Sampling Variability and "z"-Values of Parameter Estimates from Misspecified Structural Equation Models.
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Kaplan, David – Multivariate Behavioral Research, 1989
The sampling variability and zeta-values of parameter estimates for misspecified structural equation models were examined. A Monte Carlo study was used. Results are discussed in terms of asymptotic theory and the implications for the practice of structural equation models. (SLD)
Descriptors: Error of Measurement, Estimation (Mathematics), Mathematical Models, Monte Carlo Methods
A Monte Carlo Investigation of the Likelihood Ratio Test for the Number of Components in a Mixture of Normal Distributions.
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Everitt, B. S. – Multivariate Behavioral Research, 1981
Results show that the proposed sampling distribution of the test appears to be appropriate only for sample sizes above 50, and for data where the sample size is 10 times the number of variables. For such cases the power of the test is found to be fairly low. (Author/RL)
Descriptors: Mathematical Formulas, Maximum Likelihood Statistics, Monte Carlo Methods, Multivariate Analysis
The Sampling Distribution and a Test for the Significance of the Bimultivariate Redundancy Statistic: A Monte Carlo Study
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Miller, John K. – Multivariate Behavioral Research, 1975
Descriptors: Correlation, Goodness of Fit, Hypothesis Testing, Matrices
Structural Equation Modeling with Small Samples: Test Statistics.
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Bentler, Peter M.; Yuan, Ke-Hai – Multivariate Behavioral Research, 1999
Studied the small sample behavior of several test statistics based on the maximum-likelihood estimator but designed to perform better with nonnormal data. Monte Carlo results indicate the satisfactory performance of the "F" statistic recently proposed by K. Yuan and P. Bentler (1997). (SLD)
Descriptors: Estimation (Mathematics), Maximum Likelihood Statistics, Monte Carlo Methods, Sample Size
Remarks on Parallel Analysis.
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Buja, Andreas; Eyuboglu, Nermin – Multivariate Behavioral Research, 1992
Use of parallel analysis (PA), a selection rule for the number-of-factors problem, is investigated from the viewpoint of permutation assessment through a Monte Carlo simulation. Results reveal advantages and limitations of PA. Tables of sample eigenvalues are included. (SLD)
Descriptors: Computer Simulation, Correlation, Factor Structure, Mathematical Models