I am conducting a meta-analysis of cognitive-behavioural interventions on self-esteem and I could use some assistance navigating through the statistical procedures.
I have extracted pre-post scores from single group designs and experiment/control scores from RCTs and calculated g for self-esteem rating scales and depression rating scales. Based on the nature of the studies, there are four themes that I would like to explore and I would like to ensure that I use the correct statistical tools and set the correct hypotheses.
The first task is to see if there is a difference between single-day workshops and multi-session therapy over several weeks. The hypothesis is that the former will yield smaller effect sizes than the latter. Second, we understand that single-group designs tend to have larger effect sizes than RCTs and we would like to see if that is the case. Third, we would like to test to see if the three different CBT-based interventions that are used are significantly different. We believe that they will not be. Lastly, we would like to know if the level of self-esteem prior to treatment is a predictor of outcome. It is unclear as to the direction that this might take, though. It could be argued that there is a ceiling effect, which limits the impact of an intervention on people with high self-esteem, or that low self-esteem is enduring enough to resist treatment compared to those with higher self-reported self-esteem.
Would it be as simple as creating dichotomous variables for each of the above and then using a meta-regression analysis of the four variables using R or would some of them be better suited to a Z-test between subgroups? I assume that as we would see in primary studies, multiple Z-tests would lead to a rise in the overall alpha. I am mindful that the third variable (intervention) is an a priori acknowledgment of the null hypothesis and the fourth one might be too vague for inclusion, so I would appreciate any guidance on how to proceed correctly.