When to use correlation as effect size when performing meta-analysis with diverse predictors?

I am a PhD student and am conducting a literature review on musculoskeletal physiotherapy and am struggling to get guidance on this subject as most health researchers conduct meta-analyses of intervention effects/ effectiveness and not of correlations.

I am looking at the predictors of attendance and adherence to treatment recommendations in musculoskeletal physiotherapy.

• The independent variables range from nominal ones like gender, ordinal like socio economic status as well as interval ratio measurements.

Having looked at literature and discussed with a couple of people, I feel that correlation 'r' is the effect size that we would need to use, and for nominal or ordinal data, we can use 2x2 contigency tables, calculate t or chi square and then convert it to 'r'. Then we apply an 'r' to 'z' transformation to combine results and then reconvert it to 'r' for interpretation.

Is this the right approach? What other factors should be considered?

Another issue is that I am taking several different diseases like neck pain, low back pain, ankle injury etc. Is it too heterogeneous to conduct meta analysis even though they have a common symptomatic presentation?

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i don't think i understood you fully, but regarding the heterogeneity of the correlations you are trying to combine, you can always do a Q-Test to check whether or not the null hypothesis that they were all sampled from the same population makes sense (which is the usual purpose of meta analysis as i know it ...). Q-Test. Additionally i would recommend that you get old of Michael Borenstein's Introduction-Meta-Analysis-Statistics-Practice which is a very complete introductory book on Meta Analysis. –  mmgm Mar 29 '12 at 19:02

Just a few thoughts:

• If you have multiple predictors then you will probably want to perform multiple meta-analyses; one for each predictor.
• When performing the meta-analysis, you will need to think about what properties of the studies you record. If you record the type of disease and aspect of adherence, then you can perform moderator analysis to see whether the size of predictor-outcome correlations vary by the moderator. This should partially resolve the issue of combining research from multiple diseases. It becomes an empirical question as to whether the size of correlations vary by disease type.
• In general, Pearson's correlation seems like a reasonable approach to aggregating relationships. You should be able to extract information from most reports that describe bivariate relationships that allow calculation of a correlation. Dedicated meta-analysis software generally makes this task easier.
• You might want to think about the effect of measurement and scaling on obtained sizes to correlations (e.g., adherence could presumably be measured in a binary (yes, no) way or in a continuous way (e.g., degree of adherence). Correlations are typically larger when scales are either more nuanced or more reliable.
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Hi Jeromy, Thanks for your reply. I have got the Borenstien book and have started going through it. –  Dev Mar 30 '12 at 15:28
Hi Jeromy, Thanks for your reply. I have got the Borenstien book and have started going through it. Excuse my ignorance, but do I therefore, take up individual bivariate relationship, convert the metric that is provided to 'r'. But then 'r' can be calculated only for continuous data, is it OK to use other statistics like 't' or chi square and then convert it to 'r' for other measurements like gender? I have downloaded a demo copy of comprehensive meta analysis software and it does look good, though cant afford the full version. –  Dev Mar 30 '12 at 15:47