So why do the majority of studies justify their review by claiming that because I² is low, effect size variations are low? We can further use prediction intervals solely for that reason and avoid making any misleading assumptions about effect size variations. Just read "Basics of meta-analysis: I² is not an absolute measure of heterogeneity., (Borenstein et al, 2017)". Thanks for the video about it too, it was really helpful.
I2 is the ratio of true to total. So as the true variances increases, I2 tends to get larger. And as the sampling error gets smaller (the studies are larger) I2 tends to get larger
Very helpful. Thank you!
So why do the majority of studies justify their review by claiming that because I² is low, effect size variations are low? We can further use prediction intervals solely for that reason and avoid making any misleading assumptions about effect size variations. Just read "Basics of meta-analysis: I² is not an absolute measure of heterogeneity., (Borenstein et al, 2017)". Thanks for the video about it too, it was really helpful.
48'00- prediction interval
Can we calculate 95%CI for I square using CMA software? Thanks
I just want to ask, what are the factors affecting the I squared.
I2 is the ratio of true to total. So as the true variances increases, I2 tends to get larger. And as the sampling error gets smaller (the studies are larger) I2 tends to get larger