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Abstract Details
Activity Number:
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529
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Social Statistics Section
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Abstract - #301466 |
Title:
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Meta-Analysis of Multivariate Outcomes: A Monte Carlo Comparison of Alternative Strategies
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Author(s):
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Corina M. Owens*+ and Jeffrey Kromrey and Julie Gloudemans
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Companies:
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University of South Florida and University of South Florida and University of South Florida
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Address:
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4202 E. Fowler Avenue, Tampa, FL, 33620-7750, United States
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Keywords:
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meta-analysis ;
multivariate ;
random effects ;
multilevel models
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Abstract:
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Models for meta-analysis are based on the assumption that effect sizes are independent of each other. However, multiple outcomes and treatments from the same study make the tenability of this assumption doubtful. Several meta-analytic methods have been proposed to handle dependent effect sizes: a multivariate multi-level approach (Kalaian & Raudenbush, 1996); Hedges, Tipton, and Johnson's (2010) robust variance estimation strategy; and Gleser and Olkin's (2009) stochastically dependent effect size approach. This research used Monte Carlo methods to compare these approaches to a traditional univariate random effects approach (Hedges & Olkin, 1989). Factors investigated included (a) number of studies included in the meta-analysis, (b) population mean effect sizes, (c) covariance between the effect sizes, (d) within study sample size, and (e) population effect size variance. Accuracy and pr
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