I'm having trouble understanding what a correct model for my data should be, and also if it would even be a good idea to make it a single model.
At the basis it is a rather simple regression model with two predictors. The predictors are 'perceived ease of use' (ease) and 'Perceived Usefulness' (useful), and the dependent variable is 'Intention to Use' (intention).
Now the thing is: we measured these variables for 10 different technologies. So for each person we have a multiple of these variables. On top of that we have two measurements for each person (repeated measures)
If at all possible, I would like to fit this all into one model (mixed model in SPSS). Ofcourse an 'easy' solution would be to do a 'simple' repeated measure model for each technology separately, but then I think you lose a lot of information, cause you cannot partial out the effect of the person, and difference between the models would be hard to explain.
So I guess my first question is, is it valid to treat all dependent variables (intention) for the different technologies as the same outcome variable? or do we have to do 10 separate analyses? As I said the reasons to make a single model is that more information is preserved (the effect of the person), thus hopefully resulting in a more general model that is based on 'more' data.
And if it is valid to do this, I'm not sure how to model this in SPSS. Right now I have this:
MIXED intention BY personID time technology WITH ease usefulness /FIXED = usefulness ease | SSTYPE(3) /RANDOM=technology time| SUBJECT(personID) COVTYPE(ID) /REPEATED = time*technology | SUBJECT(personID) /PRINT = SOLUTION TESTCOV.
However I'm not sure this models the dependencies in the data correctly. I also don't know what kind of covariance matrices to choose.
Can anyone tell me if this model fits my description well, or what I can do to improve it? Thank you very much!