Education Guide

I remain staunchly committed to the idea that meta-analyses must deal with all studies, good bad and indifferent, and that their results are only properly understood in the context of each other, not after having been censored by some a priori set of prejudices. An effect size of 1.50 for 20 studies employing randomized groups has a whole different meaning when 50 studies using matching show an average effect of 1.40 than if 50 matched groups studies show an effect of -.50, for example. Glass 2000 Meta-Analysis at 25

It is common to acknowledge, in meta-analysis and elsewhere, that many data sets fail to meet probabilistic sampling conditions, and then to argue that one ought to treat the data in hand "as if" it were a random sample of some hypothetical population. One must be wary here of the slide from "hypothesis about a population" into "a hypothetical population." They are quite different things, the former being standard and unobjectionable, the latter being a figment with which we hardly know how to deal.

…… If the sample is fixed and the population is allowed to be hypothetical, then surely the data analyst will imagine a population that resembles the sample of data. If I show you a handful of red and green M&Ms, you will naturally assume that I have just drawn my hand out of a bowl of mostly red and green M&Ms, not red and green and brown and yellow ones. Hence, all of these "hypothetical populations" will be merely reflections of the samples in hand and there will be no need for inferential statistics. Or put another way, if the population of inference is not defined by considerations separate from the characterization of the sample, then the population is merely a large version of the sample. With what confidence is one able to generalize the character of this sample to a population that looks like a big version of the sample? Well, with a great deal of confidence, obviously. But then, the population is nothing but the sample writ large and we really know nothing more than what the sample tells us in spite of the fact that we have attached misleadingly precise probability numbers to the result. Glass 2000 Meta-Analysis at 25

The significance of this study is that it doesn't look at differences in the selection of studies for the meta-analysis or the interpretation of or extraction of data from the studies included in the meta-analysis. Every reviewer was given the same package, the same data, and the same statistical analyses of the included studies, thus eliminating this issue. Even given that, reviewers still interpreted the results of the meta-analyses very differently.

The results of this clever exercise provide just one more bit of evidence that leads me to believe that meta-analyses are nothing more than systematic reviews of the literature with attitude. That's not to say that meta-analyses of the literature aren't often useful, just as systematic reviews of the literature, are. They are in the same way that systematic reviews are: They boil down a large number of studies and suggest an interpretation. Let's just not pretend that meta-analyses are so much more objective than a systematic review as to be considered anything more. Orac The Respectful Insolence Blog

Five years ago, this vision of how research should be reported and shared seemed hopelessly quixotic. Now it seems easily attainable. The difference is the I-word: the Internet. In 1993, spurred by the ludicrously high costs and glacial turn-around times of traditional scholarly journals, I created an internet-based peer-reviewed journal on education policy analysis (http://epaa.asu.edu). This journal, named Education Policy Analysis Archives, is now in its seventh year of publication, has published 150 articles, is accessed daily without cost by nearly 1,000 persons (the other three paper journals in this field have average total subscription bases of fewer than 1,000 persons), and has an average "lag" from submission to publication of about three weeks.

Two years ago, we adopted the policy that any one publishing a quantitative study in the journal would have to agree to archive all the raw data at the journal website so that the data could be downloaded by any reader. Our authors have done so with enthusiasm. I think that you can see how this capability puts an entirely new face on the problem of how we integrate research findings: no more inaccurate conversions of inferential test statistics into something worth knowing like an effect size or a correlation coefficient or an odds ratio; no more speculating about distribution shapes; no more frustration at not knowing what violence has been committed when linear coefficients mask curvilinear relationships. Now we simply download each others' data, and the synthesis prize goes to the person who best assembles the pieces of the jigsaw puzzle into a coherent picture of how the variables relate to each other. Glass 2000