I want to perform a meta-analysis, for which I have data from 30 different experiments, each of them with two different treatments.
I have three different types experiments based on how the data are presented:
Experiments that have several years of data, and for each year there are 3 replicates per treatment. I guess in these cases I should calculate the mean value per replicate, and the overall mean per treatment would be the mean of the three replicates, and the SE will be based on the differences among replicates, hence n=3, regardless of the number of years?
Experiment block year ambient elevated
A 1 1998 234 397
A 2 1998 209 395
A 3 1998 254 347
A 1 1999 234 317
A 2 1999 249 390
A 3 1999 204 348
Experiments with several years of data, containing mean and SE per year and treatment, but I don’t know how many replicates per treatment. How can I calculate the overall mean and SE per treatment at these experiments?
Experiment year ambient ambient.SE elevated elevated.SE
B 1998 234 12 397 32
B 1999 209 14 395 23
B 2000 254 20 347 18
Experiments that already have mean and SE per treatment calculated by others. All done.
Experiment ambient ambient.SE elevated elevated.SE
C 234 12 397 32
Based on this procedure, the weights for the meta-analysis (based on SE), basically give more importance to those experiments that have a response effect more similar among replicates, and the sample size. However, there is no consideration at all of the number of years that the experiments were carried out. My question is: can I calculate the mean for each experiment as the mean across years, so that the sample size is the number of years? Is this “legal” for a publication? Example: This way an experiment with data for 10 years would have more weight that an experiment which mean is based on 6 replicate measurements but only taken once after a few months of treatment. For my analysis makes sense that longer experiments are more “important” and hence have lower variance.
Thanks for your patience.