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Rennie, Kimberly M. – 1997
This paper explains the underlying assumptions of the sampling distribution and its role in significance testing. To compute statistical significance, estimates of population parameters must be obtained so that only one sampling distribution is defined. A sampling distribution is the underlying distribution of a statistic. Sampling distributions…
Descriptors: Analysis of Variance, Estimation (Mathematics), Sample Size, Sampling
Peer reviewed Peer reviewed
Wilcox, Rand R. – Journal of Educational Statistics, 1983
The problem of determining which of several populations has the largest mean is considered. The procedure described by Dudewicz and Dalal is extended to the case of unequal sample sizes. (JKS)
Descriptors: Analysis of Variance, Nonparametric Statistics, Probability, Reliability
Peer reviewed Peer reviewed
Olejnik, Stephen – Journal of Experimental Education, 1987
This study examined the sampling distribution of the analysis of variance F ratio in the two sample cases when it followed a preliminary test for variance equality. When the population variances were equal, the sampling distribution approximated the theoretical F distribution quite well, but not when population variances differed. (JAZ)
Descriptors: Analysis of Variance, Comparative Analysis, Computer Simulation, Sample Size
Hsiung, Tung-Hsing; Olejnik, Stephen – 1994
This study investigated the robustness of the James second-order test (James 1951; Wilcox, 1989) and the univariate F test under a two-factor fixed-effect analysis of variance (ANOVA) model in which cell variances were heterogeneous and/or distributions were nonnormal. With computer-simulated data, Type I error rates and statistical power for the…
Descriptors: Analysis of Variance, Computer Simulation, Estimation (Mathematics), Interaction
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
Chou, Tungshan; Huberty, Carl J. – 1992
The empirical performance of the technique proposed by P. O. Johnson and J. Neyman (1936) (the JN technique) and the modification of R. F. Potthoff (1964) was studied in simulated data settings. The robustness of the two JN techniques was investigated with respect to their ability to control Type I and Type III errors. Factors manipulated in the…
Descriptors: Analysis of Variance, Computer Simulation, Equations (Mathematics), Error Patterns
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