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Invited Symposium

Advances in Meta-Analysis for Multivariable Linear Models

Sunday, May 24, 2009, 9:00 AM - 10:20 AM
Yerba Buena 3 - 4

Adam R. Hafdahl Chair: Adam R. Hafdahl
Washington University in St. Louis

Most research synthesists meta-analyze one or more separate bivariate associations, each quantified by a correlation or other index. We intend for this symposium to familiarize a broader audience with contemporary meta-analytic techniques better suited for forms of multivariable association that primary researchers often examine: Linear models such as path, factor, and structural equation models, and functions of correlations such as partial and multiple correlations and regression coefficients. Some familiarity with basic meta-analysis and linear-model concepts and procedures will be assumed, but the presentations are meant to be accessible to the typical psychological scientist.

S Natasha Beretvas

Demonstration of Fixed-Effects Model Alternatives for Synthesizing Correlation and Covariance Matrices
S Natasha Beretvas
University of Texas at Austin
Meta-analytic structural equation modeling (MASEM) involves synthesizing elements of correlation or covariance matrices to be used to estimate a structural equation model. Several methods have been suggested for conducting MASEM studies. Using a real dataset, these different methods will be demonstrated. Issues and dilemmas as well as directions for future research will be discussed.

Carolyn F. Furlow

Missing Data in Meta-Analytic Structural Equation Modeling
Carolyn F. Furlow
Georgia State University
In meta-analytic structural equation modeling (MASEM) each primary study contributes a matrix of correlations. Because the research question of interest to the MASEM researcher is frequently different than those of the authors of the primary studies, there are typically variables of interest that are not measured in the primary studies. This results in correlations that are missing in the matrix. This presentation will focus on the various methods for handling those missing correlations as well as some of the issues surrounding the choice of method for handling missing data for MASEM.

Adam R. Hafdahl

Meta-Analysis for Functions of Dependent Correlations
Adam R. Hafdahl
Washington University in St. Louis
Many meta-analysts who work with correlations among several variables are interested in some function of dependent correlations, such as partial or (squared) multiple correlations, regression or path coefficients, or combinations of these quantities. This presentation will cover various meta-analytic techniques for such functions, including illustrations using real data. These procedures comprise both fixed- and random-effects methods, with emphasis on the latter for heterogeneous correlation matrices, as well as inference via large-sample and bootstrap strategies.

Mike W.-L. Cheung

Modeling Multivariate Effect Sizes With Structural Equation Models
Mike W.-L. Cheung
National University of Singapore
Multivariate meta-analysis has become increasingly popular in medical, behavioral and social sciences as the outcome measures in a meta-analysis may involve more than one effect size. Structural equation models (SEMs), on the other hand, provide a flexible statistical framework to model multivariate data. This presentation proposes a new approach to model multivariate effect sizes. Fixed-, random- and mixed-effects meta-analyses can be analyzed as SEMs. The SEM approach provides an attractive alternative to conducting multivariate meta-analysis.

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