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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
Wu, Yi-Cheng; McLean, James E. – 1993
By employing a concomitant variable, researchers can reduce the error, increase the precision, and maximize the power of an experimental design. Blocking and analysis of covariance (ANCOVA) are most often used to harness the power of a concomitant variable. Whether to block or covary and how many blocks to be used if a block design is chosen…
Descriptors: Analysis of Covariance, Analysis of Variance, Computer Simulation, Correlation
Johnson, Colleen Cook – 1993
The purpose of this study is to help define the precise nature and limits of the tolerable range in which a researcher may be relatively confident about the statistical validity of his or her research findings, focusing specifically on the statistical validity of results when violating the assumptions associated with the one-way, fixed-effects…
Descriptors: Analysis of Covariance, Analysis of Variance, Comparative Analysis, Computer Simulation
Peer reviewed Peer reviewed
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