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Wang, Marilyn D. – Educational and Psychological Measurement, 1982
Formulas for estimating the population measure of effect strength are based on the assumption that sample sizes are proportional to the sizes of their respective treatment populations. Because this assumption is frequently violated, a general method of estimating effect strength for the one-factor, fixed-effects design is presented. (Author/BW)
Descriptors: Analysis of Variance, Estimation (Mathematics), Hypothesis Testing, Mathematical Models
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Lix, Lisa M.; Keselman, H. J. – Educational and Psychological Measurement, 1998
Comparison of six procedures to test for location equality among two or more groups when population variances are heterogeneous suggests that, when the variance homogeneity and normality assumptions are not satisfied, and the design is unbalanced, the use of any of these test statistics with the usual least squares estimators is not recommended.…
Descriptors: Comparative Analysis, Estimation (Mathematics), Least Squares Statistics, Research Design
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Yarnold, Paul R. – Educational and Psychological Measurement, 1996
This article addresses the characterization and circumvention of E. H. Simpson's paradox in designs involving two ordered variables and illustrates the recommended procedure using a two-sample application involving estimation of correlation and a single-subject application involving the estimation of lag(1) autocorrelation. (Author/SLD)
Descriptors: Classification, Correlation, Estimation (Mathematics), Research Design
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Hopkins, Kenneth D.; Chappell, David – Educational and Psychological Measurement, 1994
Quick power estimates for detecting a difference in two population proportions are expedient during early stages of research planning. Such estimates are tabled and graphed in this article. They are shown to be conservative but accurate for most research situations when proportions fall in the range of 0.25 to 0.75. (SLD)
Descriptors: Comparative Analysis, Data Collection, Estimation (Mathematics), Graphs
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Marcoulides, George A.; Goldstein, Zvi – Educational and Psychological Measurement, 1990
A methodology for determining the optimal number of observations to use in a measurement design when resource constraints are imposed is presented. Two- and three-facet designs are outlined. Parallel closed form formulae can easily be determined for other designs. (TJH)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Generalizability Theory, Mathematical Models