By Terri D. Pigott (auth.)
The topic of the ebook is complex statistical analyses for quantitative learn synthesis (meta-analysis), and chosen useful matters with regards to examine synthesis that aren't coated intimately within the many current introductory books on learn synthesis (or meta-analysis). advanced statistical concerns are bobbing up extra often because the basic learn that's summarized in quantitative syntheses itself turns into extra complicated, and as researchers who're carrying out meta-analyses develop into extra formidable within the questions they need to handle. additionally as researchers have won extra event in accomplishing learn syntheses, a number of key concerns have endured and now seem basic to the firm of summarizing research.
Specifically the e-book describes multivariate analyses for numerous indices generic in meta-analysis (e.g., correlations, impression sizes, proportions and/or odds ratios), will define how one can do strength research for meta-analysis (again for every of the several sorts of research final result indices), and examines matters round examine caliber and examine layout and their roles in synthesis. for every of the statistical themes we are going to research the several attainable statistical versions (i.e., fastened, random, and combined versions) which may be followed through a researcher. In facing the problems of analysis caliber and examine layout it covers a few particular themes which are of wide obstacle to analyze synthesists. in lots of fields a present factor is how one can make feel of effects while experiences utilizing a number of assorted designs look in a study literature (e.g., Morris & Deshon, 1997, 2002). In schooling and different social sciences a serious point of this factor is how one may perhaps contain qualitative (e.g., case learn) learn inside of a synthesis. In drugs, similar concerns situation no matter if and the way to summarize observational reports, and whether or not they may be mixed with randomized managed trials (or no matter if they need to be mixed at all).
For each one subject, incorporated is a labored instance (e.g., for the statistical analyses) and/or a close description of a broadcast study synthesis that offers with the sensible (non-statistical) concerns coated.
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Extra info for Advances in Meta-Analysis
In the early history of meta-analysis, the fixed effects assumption was the most common. More recently, reviewers have tended to use random effects models since there are multiple sources of variation among studies that reviewers do not want to attribute only to sampling variation. Random effects models also provide estimates with larger confidence levels (larger variances) since we assume a component of between study, random variance. The confusion over mixed and fully random effects models occurs when we are talking about effect size models with random components.
For researchers using research synthesis, power computations need to occur in the early stages of the review to determine if adequate evidence exists to test a given hypothesis. As I will discuss later, reviewers should start with substantively significant values for an average effect size and compute power given a range of values for the effect size variances, sample sizes within studies, and number of studies. The following chapters provide an overview of power analysis in meta-analysis, and illustrate its use with examples.
Both fixed and random effects models tests are discussed. In addition, examples are provided for computing the number of studies needed to detect a substantively important effect size, and the detectable effect size with a given number of studies. 1 Background Researchers planning new primary studies use power analysis to increase their odds of finding results that they believe are present. Power analysis allows the researcher to determine a priori the number of participants needed in a study to find statistical significance.