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Obsidian | Level 7

Hi all


I have two datasets addressing essentially the same question about ideal body shape and body composition.


In the first, between subjects dataset (BETWEEN), participants are assigned to one of three experimental groups: personal ideal body shape (PERS), cultural ideal body shape (CULT), and most attractive body shape (ATT). In each group, participants judge images of body composition that fit the requirements of that condition. Then, the average muscle and adipose are calculated from their decisions. We also gather information about 2 covariates: the age and actual body mass index (BMI) of the participant. 


In the second, within subjects dataset (WITHIN), each participant makes judgements about all  three conditions: personal ideal body shape (PERS), cultural ideal body shape (CULT), and most attractive body shape (ATT). The average body composition for the bodies in each of the three conditions is computed for each participant, and the same covariates, age and BMI obtained. 


It is relatively straight forward to carry out appropriately structured MANCOVA style analyses using PROC GLM, separately for the BETWEEN and WITHIN datasets. But this means that I cant directly assess the potential effect of sample type - i.e. WITHIN versus BETWEEN. There are reasons to do with psychological biases that I would like to estimate this. 


So, does anyone know if I can build a single model for the multivariate output (each decision about body composition renders both an adipose a skeletal muscle value), which  incorporates sample type as a fixed effect, along with condition (PERS, CULT, and ATT), and the two covariates. I imagine that at least one difficult bit is structuring the random effects correctly. 


Bottom line, I would be extremely grateful to anyone who can provide expert advice on this problem.


Many thanks in advance





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