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Activity Number:
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481
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Type:
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Contributed
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Date/Time:
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Wednesday, August 9, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Consulting
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| Abstract - #306121 |
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Title:
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An Empirical Power Analysis of Hierarchical Multivariate Linear Model under Three Covariance Structures in Longitudinal Data Analysis
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Author(s):
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Hua Fang*+ and Gordon P. Brooks and Maria L. Rizzo and Robert S. Barcikowski
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Companies:
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Ohio University and Ohio University and Ohio University and Ohio University
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Address:
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22 First Street, Athens, OH, 45701,
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Keywords:
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empirical power ; hierarchical ; multivariate ; longitudinal data analysis ; bootstrap
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Abstract:
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This paper examines the empirical power of hierarchical multivariate linear model (HMLM) under three covariance structures in longitudinal data analysis. The three covariance structures are called random slope with homogeneous level-1 variance, unstructured and first-order autoregressive. A stacked SAS macro is written to generate standard hierarchical multivariate data and to compute power under each covariance structure. The power is examined by varying correlation, reliability, effect size, and ratio of group sample size to time points. The bootstrap estimates for the fixed treatment effect are calculated under each covariance structure. Power patterns and bootstrap estimates under each covariance structure are compared through tables and figures. The conclusion discusses importance of covariance selection in the application of HMLM to the longitudinal data analysis.
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