I am trying to do a nested repeated measures mixed model. My design is very messy, has small sample size, and is unbalanced. I am trying to figure out how to make it work so I understand it in SPSS. I think I am close but could use a boost in confidence!

- Have I defined my terms correctly??
- Have I incorporated the nested design correctly??

Here are the basics:

I am doing a Before-After-Control-Impact design.

Treatment 1 (impact) - 2 streams - measurements taken at 3 stations per stream
Treatment 2 (control - no treatment) - 3 streams - measurements taken at 3 stations per stream

Station is nested within stream. And stream within treatment (Control/Impact).

Measurements were taken each year for 5 years (within subject factor) but there are many missing values (some stations were dry during certain years so a measurement could not be taken).

Before - 2009-2011 (3 years)
After - 2012-2013 (2 years)
- Treatment 1 was applied after 2011 measurements

I have tried building my model in SPSS like this:

(the fixed term is the interaction term I am mostly interested in between Before-After and Control Impact)

RANDOM = INTERCEPT station(stream) | SUBJECT (station) COVTYPE (VC)
REPEATED = year | SUBJECT (stream*station) COVTYPE (AR1)

- For the random factor do I want to include the intercept?
- For the random Subject Grouping do I group by stream or station? When I group by:

Station: DF = 24.127 F = 6.23 Sig = 0.003
Stream: DF = 5.784 F = 4.139 Sig = 0.068

I am thinking I will use alpha 0.10 instead of 0.05 because of the small sample size and lots of noise in my data.

Any advice or input would be so appreciated!! My other option is to average my stations to get one value per stream per year to simplify the model but my supervisor prefers me to make the nested version work to represent the data better.