Integrating Results Through Meta-Analytic Review Using SAS Software

ISBN-10: 1580252931
ISBN-13: 9781580252935
Edition: 1999
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Book details

List price: $54.95
Copyright year: 1999
Publisher: SAS Institute
Publication date: 6/1/1998
Binding: Hardcover
Pages: 400
Size: 8.75" wide x 11.00" long x 0.75" tall
Weight: 1.584
Language: English

Morgan C. Wang, Ph. D., is a statistician with a solid theoretical and applied background in meta-analysis. He has supervised several research reports using SAS software to implement meta-analytic procedures. One of these reports was presented at the SAS Users Group International Conference (1994), and another report was presented at the Joint Statistical Annual Meetings (1995). Wang has over 12 years of experience working with the SAS System and has taught SAS/GRAPH short courses and other graduate-level statistical courses using SAS software.

Brad J. Bushman is Professor of Psychology and Communication Studies at University of Michigan. He is also a professor at the Vrije Universiteit in Amsterdam (the Netherlands), where he teaches and does research two months per year. He received his Ph.D. from the University of Missouri in 1989 and has taught introductory social psychology courses for 15 years. Bushman's research, which has challenged several societal myths (e.g., violent media have a trivial effect on aggression, venting anger reduces aggression, violent people suffer from low self-esteem, violence and sex on TV sell products, warning labels repel consumers), earned him the nickname of the "Myth Buster" by one colleague. His research has been published in the top scientific journals (e.g., Science) and has been featured on television (e.g., ABC News 20/20, Discovery Channel), on radio (e.g., NPR, BBC, CBC), in magazines (e.g., Newsweek, Sports Illustrated), and in newspapers (e.g., The New York Times, Wall Street Journal). Bushman lives in Ann Arbor, Michigan, with his wife Tammy Stafford and their three children. In his spare time, he likes to ride his mountain bike and listen to jazz music.

Using This Book
Introduction
Narrative (Qualitative) and Meta-Analytic (Quantitative) Literature Reviews
Increasing Use of Meta-Analysis
Two Approaches to Conducting a Meta-Analysis
Operationally Defining Abstract Concepts in Research
Categorical (Qualitative) and Continuous (Quantitative) Variables
Types of Variables in Research
Effect-Size Measures for Categorical Variables
Effect-Size Measures for Continuous Variables
Some Issues to Consider When Conducting a Meta-Analysis
Publication Bias and Study Quality
Missing Effect-Size Estimates
Fixed- and Random-Effects Models
Correlated Effect-Size Estimates
Using the SAS System to Conduct a Meta-Analysis
References
Using the SAS System to Conduct a Meta-Analysis
Introduction
Help for New Users of SAS Software
SAS/ASSIST Software
Creating a Meta-Analytic Data Set Using SAS Software
Creating a Meta-Analytic Data Set from the SAS DATA Step
Creating a Meta-Analytic Data Set from an OBDC Data
Manipulating a SAS Data Set Using the SAS DATA Step
Renaming Variables
Keeping and Dropping Variables in Output SAS Data Sets
Creating a Permanent SAS Data Library
Using a SAS Macro
Creating and Manipulating a SAS Graph
SAS/GRAPH Displays on the Computer Monitor
Creating a SAS/GRAPH CGM File
SAS Procedures Used in This Book
PROC SORT
PROC PRINT
PROC MEANS
PROC UNIVARIATE
PROC GLM
PROC FORMAT
PROC TIMEPLOT
PROC SHEWHART
Conclusions
References
Graphical Presentation of Meta-Analytic Results
Introduction
Dot Plots
Funnel Plots
Using a Funnel Plot to Investigate Whether All Studies Come from a Single Population
Using a Funnel Plot to Search for Publication Bias
Problems with Funnel Plots
Normal Quantile Plots
Using a Normal Quantile Plot to Check the Normality Assumption
Using a Normal Quantile Plot to Investigate Whether All Studies Come from a Single Population
Using a Normal Quantile Plot to Search for Publication Bias
Problems with Normal Quantile Plots
Stem-and-Leaf Plots
Box Plots
Conclusions
References
Appendices
SAS Code for Output 3.1
SAS Macro for Finding the Minimum and Maximum Values of the Variables on the X and Y Axes
SAS Macro for Entering Parameters for a Funnel Plot
SAS Macro for Creating a Funnel Plot
SAS Code for Figure 3.2
SAS Code Used to Create Normal Quantile Plots
Combining Effect-Size Estimates Based on Categorical Data
Introduction
Two-Way Contingency Tables
The Odds Ratio [omega]
Combining Odds Ratios Using the Weighted Average Method
Heterogeneity Test for Odds Ratios
Combining Odds Ratios Using the Mantel-Haenszel Method
Controlling for the Effects of Covariates
Control by Logistic Regression
Control by Stratification
Conclusions
References
Appendices
SAS Macro for Computing the Odds Ratio
SAS Macro for Computing the Common Odds Ratio Based on the Weighted Average Method
SAS Macro for Heterogeneity Test of Odds Ratios
SAS Macro for Computing the Common Odds Ratio Based on the Mantel-Haenszel Method
SAS Code for Example 4.5
Combining Effect-Size Estimates Based on Continuous Data
Introduction
Two Families of Effect Sizes
The Standardized Mean Difference Family
The Correlation Family
Relationship between the Two Families of Effect Sizes
Converting Test Statistics to Effect-Size Estimates and Converting Effect-Size Estimators from One Type to Another
Combining Sample Standardized Mean Differences
Combining Sample Correlation Coefficients
Conclusions
References
Appendices
Formulas for Converting Cohen's d, Hedges' g, and the Point-Biseral Correlation to Hedges' g[subscript U]
Formulas for Converting Cohen's d, Hedges' g, and Hedges' g[subscript U] to Point-Biseral Correlations
SAS Macro for Computing Effect-Size Estimates
Formulas for Obtaining Hedges' g, Hedges' g[subscript U], and the Point-Biseral Correlation from a t Test Statistic
SAS Macro for Converting Test Statistics to Effect-Size Estimates and for Converting Effect-Size Estimators from One to Another
SAS Macro for Computing a Weighted Average of Effect-Size Estimates
Vote-Counting Procedures in Meta-Analysis
Introduction
The Conventional Vote-Counting Procedure
Level of Significance
Vote-Counting Situations
Vote-Counting Procedures for Estimating the Population Standardized Mean Difference
Vote-Counting Procedures for Estimating the Population Correlation Coefficient
Conclusions
References
Appendices
SAS/IML Module for Obtaining the Probability of the Vote-Counting Estimate Using the Large Sample Approximation Method
SAS/IML Module for Obtaining a Confidence Interval for the Population Standardized Mean Difference Using Vote-Counting Procedures
SAS Macro for Obtaining a Confidence Interval for the Population Correlation Coefficient Using Vote-Counting Procedures
SAS Macro for Estimating Population Effect Sizes Using Vote-Counting Procedures
Combining Effect-Size Estimates and Vote Counts
Introduction
Using the Combined Procedure to Estimate the Population Standardized Mean Difference
Using the Combined Procedure to Estimate the Population Correlation Coefficient
Conclusions
References
Appendices
SAS Code for Example 7.1
SAS Macro for Obtaining the Pearson Product-Moment Correlation Coefficient Based on the Method of Maximum Likelihood
SAS Code for Example 7.2
Fixed-Effects Models in Meta-Analysis
Introduction
Fixed- and Random-Effects Models in Individual Experiments
Fixed- and Random-Effects Models in Meta-Analysis
Testing the Moderating Effects of Categorical Study Characteristics in ANOVA Models
Fixed-Effects ANOVA Models with One Categorical Factor
Fixed-Effects ANOVA Models with Two Categorical Factors
Testing the Moderating Effects of Continuous Study Characteristics in Regression Models
Confidence Intervals for Individual Regression Coefficients
Omnibus Tests for Blocks of Regression Coefficients and Tests for Homogeneity of Effects
Multicollinearity Among Study Characteristics
Quantifying Variation Explained by Study Characteristics in ANOVA and Regression Models
Conclusions
References
Appendices
SAS Macro for Computing Q-Statistics in Fixed-Effects ANOVA Models
SAS Macro for Computing Confidence Intervals for Group Mean Effects in Fixed-Effects ANOVA Models
SAS Macro for Comparing Group Mean Effects in Fixed-Effects ANOVA Models
SAS Macro for Computing Confidence Intervals for Regression Coefficients in Fixed-Effects Models
Random-Effects Models in Meta-Analysis
Introduction
Testing the Moderating Effects of Categorical Study Characteristics in ANOVA Models
Random-Effects Models with One Categorical Factor
Random-Effects Models with Two Categorical Factors
Testing the Moderating Effects of Continuous Study Characteristics in Regression Models
Testing Whether the Random-Effects Variance is Zero
Estimating the Random-Effects Variance
Confidence Intervals for Individual Regression Coefficients
Omnibus Tests for Blocks of Regression Coefficients
Multicollinearity among Study Characteristics
Conclusions
References
Combining Correlated Effect-Size Estimates
Introduction
Combining the Results from Multiple-Treatment Studies
Combining the Results from Multiple-Endpoint Studies
Conclusions
References
Appendices
SAS Macro for Computing F[subscript MAX] Statistics for Multiple-Treatment Studies
SAS Macro for Computing Combined Effect-Size Estimates and 95% Confidence Intervals in Multiple-Treatment Studies
SAS Macro for Computing Combined Effect-Size Estimates and 95% Confidence Intervals in Multiple End-Point Studies
Conducting and Reporting the Results of a Meta-Analysis
Introduction
Reporting the Results of the Literature Search
Reporting the Results of the Data Collection
Reporting the Results of the Data Analysis
Checking Statistical Assumptions
Reporting the Results of Subgroup Analyses
Reporting the Results of Sensitivity Analyses
Example of a Meta-Analysis
Reporting the Results of the Literature Search
Reporting the Results of the Data Collection
Reporting the Results of the Data Analysis
Conclusions about Example Meta-Analysis
Conclusions
References
Index

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