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Dear all,I am using the programme "Comprehensive Meta-Analysis" (CMA) to meta-analyze the effectiveness of a particular intervention. I want to meta-analyze whether there are effective changes in some hypothesized outcomes after joining the intervention. I would like to seek your advice on which effect size measure(s) to use appropriately.I wanted to use Cohen’s d as the effect size measure using r. However, my whole batch of those included studies got two types — one type with both intervention and control groups (A or B below); and another type with only intervention group (C below). It means now I mainly got three types of statistical data for effect size calculations:A) pre-post change scores of BOTH intervention group AND control groupB) post-ONLY data of both intervention group and control groupC) paired pre-post changes of ONLY intervention groupFor A and B, I am using Cohen’s d. But for C, should I use Cohen’s dz, which is the standardized paired difference?However, my principal question is, Is it statistically and conceptually acceptable to lump studies of A, B, and C together as a batch and use Cohen’s d throughout?Second, some the built-in formula in CMA require the "mean change" or "difference in means". Is "mean change" here refers to only the change in scores between two time-points within the same group? Is "difference in means" here referring to only the difference in scores between two independent groups?Some studies reported the difference / change in means directly, while some other studies only reported the scores (e.g. means with SD) at each time-points respectively. For example, if I calculate the different in means between post-test and pre-test manually myself, does this difference equal to the "mean change" or "difference in means" as required by CMA mentioned above?Any advice or recommended resources for relevant effect size calculation issues here would be highly appreciated. Thanks a lot in advance!Regards,KevriaSee More

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Dear Mike and all,I want to examine the factor structure of two scales respectively (scale A: 14 items; scale B: 26 items). We are trying several methods: CFA followed by EFA; Rasch analysis and IRT respectively. IRT seems to be a more and more popular methodology for the purpose. But I have also come across some studies using meta-SEM to examine the factor structure. It basically refers to: getting the pooled inter-item correlation matrix from the respective inter-item correlation matrix from a batch of related study, then running SEM on the pooled matrix for testing the factor structure.For meta-SEM, is item-level intercorrelation is must? Can it be based on intercorrelations among subscales instead of all individual items?Which method would be relatively more rigorous? Any recommended examples or resources? Thanks a lot!Much thanks,KevriaSee More

Tips and techniques to help meta-analysts calculate effect sizes and the variances that are needed for the purpose of analysis.See More