From the course: Meta-analysis for Data Science and Business Analytics
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Standardized mean difference: Independent groups
From the course: Meta-analysis for Data Science and Business Analytics
Standardized mean difference: Independent groups
- [Narrator] Another, perhaps more typical situation, arises when a variety of measures are used as outcome variables in the literature on a given phenomenon. For example, medical researchers might employ the results of blood pressure tests as well as cholesterol measures to assess the results of an experimental medication. It makes no sense to try to simply take an average of a blood pressure test with a measure of cholesterol levels, particularly when the scales used by the measures are likely to be quite different. In that sort of case, standardizing the outcome measure by means of a formula for effect size has multiple benefits. Not only do you deal with the problem of different scale characteristics such as mean and standard deviation, but you're also able to assess the effects of a program or treatment in different broadly defined demands. For example, you could use a meta-analysis to synthesis the results of studies that measure the subject's cholesterol levels as measured by…
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