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MacDonald, Paul – Journal of Experimental Education, 1999
Assessed the relative merits of the Student "t" test and the Wilcoxon rank sum test under four population distributions and six sample-size pairings through Monte Carlo methods. The Wilcoxon rank sum test demonstrated an advantage in statistical power for nonnormal distributions (but not normal distributions), with fewer Type III errors…
Descriptors: Monte Carlo Methods, Nonparametric Statistics, Power (Statistics), Simulation

Wilcox, Rand R. – Psychometrika, 1994
A generalization of the usual random-effects model based on trimmed means is proposed. The resulting test of no differences among J randomly sampled groups has advantages in terms of Type I errors and can yield gains in power when distributions have heavy tails and outliers. (SLD)
Descriptors: Analysis of Variance, Equations (Mathematics), Models, Power (Statistics)
Althouse, Linda Akel; Ware, William B.; Ferron, John M. – 1998
The assumption of normality underlies much of the standard statistical methodology. Knowing how to determine whether a sample of measurements is from a normally distributed population is crucial both in the development of statistical theory and in practice. W. Ware and J. Ferron have developed a new test statistic, modeled after the K-squared test…
Descriptors: Monte Carlo Methods, Power (Statistics), Sample Size, Simulation

Wilcox, Rand R. – Psychometrika, 1992
A method of comparing one-step M-estimates of location for heavy tailed distributions is proposed and investigated. Simulations indicate that the new procedure provides good control over Type I errors and has more power than do some other methods for dealing with heavy tailed distributions. (SLD)
Descriptors: Comparative Analysis, Estimation (Mathematics), Experimental Groups, Mathematical Models
Meijer, Rob R.; van Krimpen-Stoop, Edith M. L. A. – 1998
Several person-fit statistics have been proposed to detect item score patterns that do not fit an item response theory model. To classify response patterns as not fitting a model, a distribution of a person-fit statistic is needed. The null distributions of several fit statistics have been investigated using conventionally administered tests, but…
Descriptors: Ability, Adaptive Testing, Foreign Countries, Item Response Theory

Penfield, Douglas A. – Journal of Experimental Education, 1994
Type I error rate and power for the t test, Wilcoxon-Mann-Whitney test, van der Waerden Normal Scores, and Welch-Aspin-Satterthwaite (W) test are compared for two simulated independent random samples from nonnormal distributions. Conditions under which the t test and W test are best to use are discussed. (SLD)
Descriptors: Monte Carlo Methods, Nonparametric Statistics, Power (Statistics), Sample Size
Reshetar, Rosemary A.; Swaminathan, Hariharan – 1992
This study compared the model of J. E. Grizzle, C. F. Starmer, and G. G. Koch (GSK, 1969) and log-linear model-based approaches for testing hypotheses in r x c contingency tables. Tables were simulated under various conditions of table, sample, row-effect size, and column-effect size. Test statistics for column (main) and interaction effects were…
Descriptors: Chi Square, Classification, Comparative Analysis, Effect Size

Smith, Richard M. – Educational and Psychological Measurement, 1994
Simulated data are used to assess the appropriateness of using separate calibration and between-fit approaches to detecting item bias in the Rasch rating scale model. Results indicate that Type I error rates for the null distribution hold even when there are different ability levels for reference and focal groups. (SLD)
Descriptors: Ability, Goodness of Fit, Identification, Item Bias
Becker, Betsy Jane – 1986
This paper discusses distribution theory and power computations for four common "tests of combined significance." These tests are calculated using one-sided sample probabilities or p values from independent studies (or hypothesis tests), and provide an overall significance level for the series of results. Noncentral asymptotic sampling…
Descriptors: Achievement Tests, Correlation, Effect Size, Hypothesis Testing