I have compiled a very small set of summary data from the literature, and I wish to compare the variances between aspects of the literature-based data, and to some of my own data. The summary data includes the mean, standard deviation and sample size.
In earlier tests, I compared the variances of one continuous dependent variable among 2 age classes and 2 years. I used the Fligner-Killeen test since I have extreme values in the data and I'm not sure it was normal (can't remember now!). I followed up this broad test with pairwise multiple comparisons using F-tests in R
What would be a good way to compare the variances of the literature-sourced data? I've searched through the help files in R, and came up with this method, which I believe generates a random set of numbers with the specified sample size, mean and standard deviation, and then I used those datasets to conduct the F-test:
herring_year1<-rnorm(10,mean=10.5,sd=0.51) herring_year2<-rnorm(15,mean=10.9,sd=0.43) var.test(herring_year1,herring_year2)
Would this be a good approach? If not, can you suggest what might be? If so, how can I then compare these variances to my own data set? Should I essentially use the summary data from my own set in the same manner? Or generate the random data for the summary data from the literature and stick it in a file to compare to my raw data?
Also, do I need a broad test initially, or can I go straight to the pairwise comparisons?