Presenter: Professor Bimal Sinha, Department of Mathematics & Statistics, University of Maryland


Statistical meta-analysis deals with a variety of sophisticated statistical methods to efficiently combine the results of several studies all with a common target. Examples of such studies abound in the literature. Some common application areas include gender studies in education, EPA studies of effects of second hand smoking on women, and controlled or comparative trials in medicine and epidemiology. The target unknown parameter in meta-analysis is often referred to as EFFECT SIZE.

Some common examples are: difference of two means, difference or ratio of two proportions, correlation coefficient, odds ratio, etc. In this course, we will first describe the basic concepts of effect size for continuous measurements as well as qualitative attributes and their estimation/test/confidence interval construction. .Next, statistical methods of combination of tests and estimates of effect size from various independent studies will be explained. This will be followed by a discussion of tests for homogeneity of effect sizes, fixed versus random effects model of meta-analysis, combination of Gallup polls, meta-analysis of binary data, meta-regression, and publication bias. The common data situation in meta-analysis is the availability of only published data like effect size estimate plus standard error or effect size estimate plus confidence interval. Many real data sets of this type will be presented and analyzed covering the fields of educational research and health sciences. Some computational aspects of meta-analysis using standard statistical software R  will be briefly mentioned. No computer demonstration will be given.

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Intermediate R Workshop

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