POWPAL: A program for estimating effect sizes, statistical power, and sample sizes. Educational & Psychological Measurement, 42, 521–526. Simplified determinations of statistical power, magnitude of effect, and research sample sizes. Behavior Research Methods, Instruments, & Computers, 28, 1–11.įriedman, H. GPOWER: A general power analysis program. Unpublished doctoral dissertation, University of British Columbia.Įrdfelder, E., Faul, F., & Buchner, A. The interaction effects of data categorization and noncircularity of the sampling distribution of generalizability coefficients in analysis of variance models. Computational procedures for estimating magnitude of effect for some analysis of variance designs. Univariate versus multivariate tests in repeated-measures experiments. Statistical power analysis for the behavioral sciences (3rd ed.). Behavior Research Methods, Instruments, & Computers, 28, 319–326.Ĭohen, J. Statistical power in complex experimental designs. Behavior Research Methods, Instruments, & Computers, 30, 462–477.īradley, D. Some cautions regarding statistical power in split-plot designs. Statistical Power Analysis: A Computer Program. Los Angeles: University of California Press.īorenstein, M., & Cohen, J. Support for the accuracy of these formulae is given, thus allowing for direct analytic power calculations in future studies.īMDP (1988). Equations for estimating the error variance of each test of the two-way model were constructed by examining power and mean square error trends across different correlation matrices. Results indicated that the greater the magnitude of the differences between the average correlation among the levels of Factor A and the average correlation in the AB matrix, the lower the power for Factor B (and vice versa). Monte Carlo simulation procedures were used to estimate power for the A, B, and AB tests of a 2×3, a 2×6, a 2×9, a 3×3, a 3×6, and a 3×9 design under varying experimental conditions of effect size (small, medium, and large), average correlation (.4 and. The main objective of this study was to determine the effect of the correlation between the levels in one RM factor on the power of the other RM factor. Determining a priori power for univariate repeated measures (RM) ANOVA designs with two or more within-subjects factors that have different correlational patterns between the factors is currently difficult due to the unavailability of accurate methods to estimate the error variances used in power calculations.
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