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Olejnik, Stephen F.; Algina, James – 1985
The present investigation developed power curves for two parametric and two nonparametric procedures for testing the equality of population variances. Both normal and non-normal distributions were considered for the two group design with equal and unequal sample frequencies. The results indicated that when population distributions differed only in…
Descriptors: Computer Simulation, Hypothesis Testing, Power (Statistics), Sampling
Peer reviewedCohen, Ayala – Psychometrika, 1986
This article proposes a method for testing equality of variances which exploits Pitman's idea and the computational power of simulations. Several advantages to this method are illustrated. A Monte Carlo study for several combinations of sample sizes and number of variables is presented. (Author/LMO)
Descriptors: Analysis of Covariance, Computer Simulation, Correlation, Hypothesis Testing
Peer reviewedRasmussen, Jeffrey Lee – Evaluation Review, 1985
A recent study (Blair and Higgins, 1980) indicated a power advantage for the Wilcoxon W Test over student's t-test when calculated from a common mixed-normal sample. Results of the present study indicate that the t-test corrected for outliers shows a superior power curve to the Wilcoxon W.
Descriptors: Computer Simulation, Error of Measurement, Hypothesis Testing, Power (Statistics)
Peer reviewedWoodruff, 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


