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Olejnik, Stephen F.; Algina, James – Journal of Educational Statistics, 1984
Using computer simulation, parametric analysis of covariance (ANCOVA) was compared to ANCOVA with data transformed using ranks, in terms of proportion of Type I errors and statistical power. Results indicated that parametric ANCOVA was robust to violations of either normality or homoscedasticity, but practiced significant power differences favored…
Descriptors: Analysis of Covariance, Computer Simulation, Hypothesis Testing, Nonparametric Statistics
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Fouladi, Rachel T. – Structural Equation Modeling, 2000
Provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and details Monte Carlo simulation results on Type I and Type II error control. Demonstrates through the simulation that robustness and nonrobustness of structure analysis techniques vary as a…
Descriptors: Analysis of Covariance, Correlation, Monte Carlo Methods, Multivariate Analysis
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Bentler, Peter M. – Psychometrika, 1983
Current practice in structural modeling of variables is limited to means and covariances based on multivariate normality assumptions. This article extends structural equation models to higher order product moments and to non-normal distributions. Areas of possible research are described. (Author/JKS)
Descriptors: Analysis of Covariance, Factor Analysis, Least Squares Statistics, Mathematical Models
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Woodruff, David J.; Feldt, Leonard S. – Psychometrika, 1986
This paper presents 11 statistical procedures which test the equality of m coefficient alphas when the sample alpha coefficients are dependent. Several of the procedures are derived in detail, and numerical examples are given for two. (Author/LMO)
Descriptors: Analysis of Covariance, Analysis of Variance, Computer Simulation, Hypothesis Testing