Hi Mike and all,
I'm working on a meta-analysis examining the mediators between attachment and intimate partner aggression. I would like to use two methods to meta-analyze the data. The methods include 1) three-level meta-analytic models (Assink & Wibbelink, 2016) and 2) three-level TSSEM (Wilson et al., 2016). Both are random-effects models.
However, there are some missing correlations in the included studies. In particular, a few studies did not report any correlations at all. My meta-analysis contains only a small number of studies because many mediators have been examined by only a handful of studies. Some mediators only got 3 studies; whereas others got 9 studies.
Would you please advise what is the best way to handle missing correlations in both analyses, especially in the context of small number of studies?
For the three-level TSSEM, would you please confirm whether the missing data could be handled automatically as in traditional TSSEM? I saw that Mike's paper (Cheung 2014) has mentioned this, but it was mainly for traditional TSSEM, not for the three-level one. Would you please clarify whether the method to handle missing data in a three-level random effect TTSEM is the same as that of the traditional TSSEM?
For method 1 (three-level meta-analytic models), I've searched for papers on handling missing data for multi-level meta-analysis. However, there was very little relevant paper on this topic (i.e., for multi-level synthesis). Would you mind shedding light on it when you have time? E.g., would it be preferable to exclude those studies with missing data?
I look forward to hearing from you soon.
Thank you for your time and help in advance.
Best wishes,
Iana