Hi, I have been using R to do multiple analyses of variance with two covariates, but recently found that the results in SPSS were very different. I have check several books and web resources and I think that both methods are correct, but I am less familiar with R, so I was hoping someone could offer some suggestions. Oddly simple ANOVA is the same in SPSS and R. Including covariates improves the main effect (p-value) in R and diminishes it in SPSS..
The formula I have been using is: >Y = cbind(dV1, dV2, dV3) >aov(lm(Y~iV1+cV1+cV2)) The main independent variable is disease group and the covariates are continuous nuisance variables such as age. Both nuisance variables interact with the dependent variable but not each other. The frequency distribution of the covariates is similar for each group, but the groups are not matched 1 to 1. Therefore we would like to control for these factors statistically. Is this the proper formula for such a test? If so, what might be cause of major discrepancy with SPSS? Thanks ahead of time Best, Jon ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
