I have data from a study where participants completed 3 interventions in a randomized order (1 week washout period), and where in every intervention, outcome measures were taken every 15 minutes over the intervention duration (3 time points). I therefore have two levels of repeated measures. My data in long form in SPSS looks like this:
(and so on for other participants, with some sparse missing data as shown in example)
I'm trying to see the effect of the different conditions, as well as time and interaction between these factors on my outcome using a linear mixed model in SPSS. However, I am confused as to how I should input this into SPSS. I've tried putting both condition and time as repeated measures with unstructured covariance type, and this leads to the "Model cannot be fitted because number of observations is less than or equal to number of model parameters" warning.
I currently have Time, Intervention, and Time*Intervention as fixed effects in my model, and ID as a random effect, due to a random intercept between participants.
Is this a problem with the way my data is organised, how I'm running the model, or both?