Contents:
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This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. Meta-analysis has .
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Having contributed chapters to two books on meta-analysis, she co-edited Publication Bias in Meta-Analysis. Larry Hedges, University of Chicago A pioneer in meta-analysis, Professor Hedges has published over 80 papers in the area many describing techniques he himself developed, that are now used as standard , co-edited the Handbook for Synthesis Research , and co-authored three books on the topic including the seminal Statistical Methods for Meta-Analysis.
He has also taught numerous short courses on meta-analysis sponsored by various international organizations such as the ASA. He works closely with the Cochrane Collaboration and is an editor of the Cochrane Handbook. He has much experience of teaching meta-analysis, both at Cambridge University and, by invitation, around the world. Request permission to reuse content from this site. Added to Your Shopping Cart.
Description This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. Meta-analysis has become a critically important tool in fields as diverse as medicine, pharmacology, epidemiology, education, psychology, business, and ecology. Permissions Request permission to reuse content from this site.
Introduction Are the studies similar enough to combine? Can I combine studies with different designs? Or perhaps I might want to know whether or not a particular new medication works better than the old one.
I look at all the studies that have been done on it, see if we should switch to different treatment options. Ultimately, the way scientists try to understand these issues is by conducting a meta-analysis, by looking across all the studies. It gives us the answers to be able to move.
Well, so how do we get there? You've got to pick one specific area you want to know across all these studies, what are we finding? For example, one time I looked at whether or not a particular theory of health communication was able to predict that people actually choose healthier lifestyles. Another one I wanted to know whether or not men and women react to infidelity differently.
The goal of this stage is try to choose a target relationship between exactly two variables. Now, meta-analysis can be done in a variety of sets of variables, but any one meta-analysis. Say, for example, owning a cat or not and how happy you are. You can do another meta-analysis on whether or not dog ownership makes you happier. But it's a separate study. So any given one meta-analysis has to be on exactly two variables alone. And once you've picked your two variables, you can then move on to the next stage. In this stage, you're trying to find as many reports as possible looking at that target pair of variables.
You want to know how many studies we got. You want to be able to find them at any way you possibly can. Also, some researchers might try to reach out to other investigators and researchers and say, do you have any articles you haven't published? We could add it to our meta-analysis. You might even email academic listserv that shoot emails out to all the researchers in a given area to try to find out if there's any unpublished articles.
I personally like to create a spreadsheet, just enter all the information in myself. But there are a variety of commercially available meta-analysis software packages that allow you to do it into those packages that give you answers more quickly and perhaps more easily. Now, what do we actually-- first thing we try to look at is the effect size for each study [effect size] in our big set of studies. Now, a regular effect size indicates a relation between two variables, but sometimes there. So if I ask people on a scale of 1 to how happy are you after owning a cat versus somebody on a scale of 1 to 50, 5 points higher means something different on each one.
So a standardized effect size allows us to standardize across all those different measurement metrics and compare the studies across each other.
It allows us to average them later. And the other thing we might want to look at. There's a variety of them. The three most common in my experience are odds ratios, Pearson Correlations, and Cohen's d. Now, some articles report these for you. And all you have to do is copy then right down. The article will report a correlation of 0. But sometimes they don't report those effect sizes. You have to actually hand calculate them. Now, any good meta-analysis software or meta-analysis book can offer you a variety of formulas to help you convert those.
For example, the t-test can be easily converted to correlation, as can a set of mean to standard deviations.
Now, in addition to recording standardized effect sizes for each study, you also have to record the sample size so we can weight that later. But also you want to record any moderators that might change the size that effect, at least. For one example, one might try to say, OK. Well, how are we measuring how happy cat owners are or non-cat owners? We might just ask them, knock on door, scale of 1 to You might peer into their windows and see how happy do they look in there.
You might look at their social media and see how happy their posts are. And perhaps these different measurement methods might give us different kinds of answers.
For example, we might also look at the sample itself. Are they cat owners in the United States, cat owners from other countries? Are they people who have had cats for a long time? Is this their first cat? There are a variety of different ways to try to break up these types of studies to see whether or not these other variables might affect the relation between these two variables. I conducted meta-analysis once on whether or not strong arguments are more persuasive than weak arguments.
And one of the key moderators was, well,. If they aren't thinking too carefully, the research tended to notice that they didn't actually respond to a strong argument better than weak argument.