![]() ![]() (1992) Pragmatic, unifying algorithm gives power probabilities for common F tests of the multivariate general linear hypothesis. (ed) Applied Analysis of Variance in Behavioral Science. (1993) Unified power analysis for t-tests through multivariate hypotheses. Journal of the American Statistical Association 87:1209–1226 (1992) Power calculations for general linear multivariate models including repeated measures applications. Muller K.E., LaVange L.M., Ramey S.L., Ramey C.T. Computational Statistics and Data Analysis 2:143–158 (1984) Practical methods for computing power in testing the multivariate general linear hypothesis. (1982) Aspects of Multivariate Statistical Theory. ![]() ![]() Educational and Psychological Measurement 61:650–667 (2001) Confidence interval, power calculation, and sample size estimation for the squared multiple correlation coefficient under the fixed and random regression models: A computer program and useful standard tables. (1974) F approximations to the distribution of Hotelling’s T 2 0. British Journal of Mathematical and Statistical Psychology 54:1–20 (2001) The analysis of repeated measures designs: a review. (1998) Testing treatment effects in repeated measures designs: An update for psychophysiological researchers. (2003) Adjusting power for a baseline covariate in linear models. The results update and expand upon current work in the literature. Monte Carlo simulation studies are conducted to assess the accuracy using a child’s intellectual development model. A treatment of multivariate analysis of covariance models is employed to demonstrate the distinct features of the proposed extension. The major modification involves the noncentrality parameters associated with the F approximations to the transformations of Wilks likelihood ratio, Pillai trace and Hotelling-Lawley trace statistics. ![]() Using analytic justification, it is shown that the proposed methods extend the existing approaches to accommodate the extra variability and arbitrary configurations of the explanatory variables. The emphasis is placed on the practical situation that not only the values of response variables for each subject are just available after the observations are made, but also the levels of explanatory variables cannot be predetermined before data collection. Hillsdale, NJ: Lawrence Earlbaum Associates.This article considers the problem of power and sample size calculations for normal outcomes within the framework of multivariate linear models. Statistical power analysis for the behavioral sciences (2nd ed.). A medium effect size ( f²=.35), and a total sample of 45 (15 in each fitness level group) provided. A medium effect size ( f²=.15), and a total sample of 45 (15 in each fitness level group) provided 9% power (power=.0945) to detect difference at the 0.05 significance level ( F (10, 78)=1.9544).Ī MANCOVA, covarying for age, with one between-subjects factor (fitness level) was used to analyze the dynamic pulmonary function variables from the GXT which included peak VO 2, VE max, V D/V T, DI, E max/VCO 2, and RR/VE max. A MANCOVA, covarying for age, with one between-subjects factor (fitness level) was used to analyze the dynamic pulmonary function variables from the GXT which included peak VO 2, VE max, V D/V T, DI, VE max/VCO 2, and RR/VE max. ![]()
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