# Appropriate homogeneity test for meta-analysis

I'm looking at measures from different studies (for a meta-analysis) and hoping to provide an aggregate effect size. I'm trying to identify hetero-/homo-geneity to determine whether I should use a random or fixed effects model. I do not have the original data - but rather effect sizes calculated from the individual studies. As such, I have Cohen's d, variance, n1, and n2 for each study/measure.

With this data, (how) can I calculate Cochran's q (or other test for homogeneity)?

Do I need to? Anderson-Darling shows that it's normally distributed.

• This link has useful information and a calculation tool (to do exactly what I hoped for - determine homogeneity of Cohen's d values - but the link to the tools is down. arxiv.org/abs/0906.2999 – d-cubed Apr 23 '14 at 0:49
• Here's the article with a working link. ncbi.nlm.nih.gov/pubmed/20528863 – d-cubed Apr 23 '14 at 1:00
• I have a doubt that either of the two - hetero-/homo-geneity determine whether I should use a random or fixed effects model. – Subhash C. Davar Aug 1 '20 at 12:30

The R package metafor is very useful for conducting a meta-analysis. If you estimate a model using rma.uni you can get a $Q$-test as output with the model to assess the heterogeneity with.
• The test might also be implemented in rma.uni for method = "FE". I think you still get the heterogeniety test from the fixed effects model. In any event, coming from rma.uni, the test is valid as long as the sample is large enough (small samples affect the heterogeneity tests badly). But it looks like you have heterogeneity. As a side note, you also get some other tests like the $I^2$ in that package. – Deathkill14 Apr 22 '14 at 19:39