I am tasked with analyzing a preexisting dataset. The experiment is fairly simple, but I'm finding that analyzing the data is challenging due to the fact that the experimental design is not ideal for analysis....
The data example (wide format):
Subj Depend_1 Depend_2 Depend_3 Covar_1 Covar_2 Age Group A 10 15 16 2 3 20 1 B 11 13 NA 3 NA 31 1 C 7 13 15 4 4 37 2 ...
Subj = Subject
Depend_1 thru Depend_3 = repeated within-subjects variable. Depend_1 was pre-treatment, Depend_2 was post treatment, Depend_3 was 1 month post treatment.
Covar_1 and 2 = participant scores on a different test, Covar_1 was taken at pre treatment, Covar_2 was taken 1 month post treatment, there was no Covar test taken at post treatment. I'm interested in using this variable as a covariate.
Age = Subject age at test(s), I would also like to use this as a covariate.
Group = Between-subjects variable, where participants were randomly assigned to a treatment or control group.
I would like to run either an MLM or ANCOVA with this data. But I'm not sure how to format it for analysis. I have 3 DV measurements for each subject, 2 measurements for Covar, and 1 for age...
Would it be sensible to use Depend_1 as a covariate, since it is a pre-treatment variable? Meaning that, if I were to format this in long format, it would look like:
Subj Time DV Covar PreTest Age Group A 1 15 2 10 20 1 A 2 16 3 10 20 1 B 1 13 3 11 31 1 C 1 13 4 7 37 2 C 2 15 4 7 37 2
Or is there another way I should handle the mismatched measurements in the data?
Hopefully my explanation is clear, and any help or referrals will be greatly appreciated!