Hello, I have data that I must analyse using mediation/path analysis. Simple enough. However, each variable was measured four times (at six month intervals). I also wish to use clustering. I know that I can do the longitudinal and path analysis in the 'within' model. I think I can understand how roughly how the model will look but trying to tease out the details is not so easy.
Specifically, I have 'intervention group' as a grouping variable. I want to regress 'physical activity' (measured at four time points) on 'intervention group'. Then I want to use 'physical activity' to predict 'BMI' (again, measured at four time points). I also want to control for effects at the cluster level.
Do I regress the i & s of the 'physical activity' on 'int. group' and then regress the i & s of 'BMI' on both 'int. group' and i & s for 'physical activity'? Or do I create latent categorical variables for each i & s for each longitudinal observed variable and regress those?
The model above looks fine. I would suggest however fitting each process separately without any ON statements as a first step. If you add another mediator, you can use WITH to specify a residual covariance.
You mediators are i1 s1 i2 s2. All residual covariances among them should be included in the model. If you add mediators y1 and y2, their residual covariances with the other mediators and themselves should also be included.