By Terri D. Pigott (auth.)

The topic of the booklet is complex statistical analyses for quantitative learn synthesis (meta-analysis), and chosen useful matters on the subject of learn synthesis that aren't lined intimately within the many present introductory books on study synthesis (or meta-analysis). advanced statistical concerns are coming up extra often because the fundamental learn that's summarized in quantitative syntheses itself turns into extra complicated, and as researchers who're accomplishing meta-analyses turn into extra formidable within the questions they want to handle. additionally as researchers have received extra event in accomplishing learn syntheses, a number of key matters have continued and now seem primary to the company of summarizing research.

Specifically the booklet describes multivariate analyses for a number of indices wide-spread in meta-analysis (e.g., correlations, influence sizes, proportions and/or odds ratios), will define the way to do strength research for meta-analysis (again for every of the various forms of research end result indices), and examines matters round study caliber and examine layout and their roles in synthesis. for every of the statistical themes we are going to study the several attainable statistical versions (i.e., fastened, random, and combined types) which may be followed via a researcher. In facing the problems of analysis caliber and learn layout it covers a couple of particular subject matters which are of huge drawback to investigate synthesists. in lots of fields a present factor is find out how to make feel of effects whilst reports utilizing a number of various designs seem in a examine literature (e.g., Morris & Deshon, 1997, 2002). In schooling and different social sciences a serious point of this factor is how one could include qualitative (e.g., case learn) study inside of a synthesis. In drugs, similar matters crisis 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 every one subject, incorporated is a labored instance (e.g., for the statistical analyses) and/or a close description of a printed study synthesis that bargains with the sensible (non-statistical) concerns lined.

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**Extra resources for Advances in Meta-Analysis**

**Sample text**

R. Rothstein. 2010. How many studies do you need? A primer on statistical power in meta-analysis. Journal of Educational and Behavioral Statistics 35: 215–47. Chapter 4 Power Analysis for the Mean Effect Size Abstract This chapter provides methods for computing the a priori power of the test of the mean effect size. 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.

As Borenstein et al. (2009) point out, the estimates for the variance component are biased when samples are small. 0957 to compute the random effects ANOVA. While the state tests have a mean Fisher’s z-score that is larger than that for the standardized tests, their confidence intervals do overlap indicating that these two means are not significantly different. 5 Confounding of Moderators in Effect Size Models As mentioned earlier, there are several examples of meta-analyses that do not follow standard statistical practice.

The third table presents the estimated regression coefficients. As many textbooks point out (Borenstein et al. 2009; Konstantopolous and Hedges 2009; Lipsey and Wilson 2000), the standard errors and tests of significance for the coefficients printed in many weighted regression routines in common statistical packages are incorrect since they are based on a model slightly different than the one used for fixed-effects meta-regression. The corrected standard errors and tests of significance must be computed by hand.