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• W1485383567 startingPage "101" @default.
• W1485383567 abstract "The power properties of traditional repeated measures and hierarchical linear models have not been clearly determined in the balanced design for longitudinal studies in the current literature. A Monte Carlo power analysis of traditional repeated measures and hierarchical multivariate linear models are presented under three variance-covariance structures. Results suggest that traditional repeated measures have higher power than hierarchical linear models for main effects, but lower power for interaction effects. Significant power differences are also exhibited when power is compared across different covariance structures. Results also supplement more comprehensive empirical indexes for estimating model precision via bootstrap estimates and the approximate power for both main effects and interaction tests under standard model assumptions." @default.
• W1485383567 created "2016-06-24" @default.
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• W1485383567 date "2008-05-01" @default.
• W1485383567 modified "2023-10-16" @default.
• W1485383567 title "A Monte Carlo Power Analysis of Traditional Repeated Measures and Hierarchical Multivariate Linear Models in Longitudinal Data Analysis" @default.
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• W1485383567 doi "https://doi.org/10.22237/jmasm/1209614880" @default.
• W1485383567 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4213952" @default.
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• W1485383567 hasPublicationYear "2008" @default.
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