I am measuring depression changes over time, and I am working with a multi-site dataset, which includes 5 sites and about 100-200 people at each site, N=800, and repeated measures across three time points.

To control for site variation, non-equivalence of interventions,  and adjust for within-person correlation over time, I am proposing hierarchical linear models for repeated measurements to assess changes in severity of depression.

Using SPSS version 19, hierarchical linear modeling with repeated measures will be conducted to test for site location interaction with depression since the intervention at each site was non-equivalent. Data will be nested in two levels (client data are at level 1 and site data are at level 2) to account for site variation. Since there is possible clustering within the sites, site location will be set to random effects. The predictor will be defined as the intervention (i.e., integrated care or enhanced referral). The dependent variable is defined as scores on the CES-D.

However, I do not think I have a large enough sample at level two (n=5). Is it possible to set the site location as a covariate and random effect in the model? By doing so, have I truly adjusted for site differences? I am not interested in the sites, just the people.

Does anyone have any recommendations to justify the level 1 and level 2 sample size?