If your assumption is that there is dependence between the different questions, ie that they are associated with the same "construct" then it makes beautiful sense to use repeated measures modelling.

I would make sure in a first step to make sure to transform the variables to the same direction and size:

• you may have completely different variance and offset, so I would centre and scale the variables first.
• anxiety and contentment are obviously going in opposite direction. So after catering and scaling it would make sense to multiply opposing variables by -1.

After that you can perform a rm model either using individual as random factor or by marginal modelling (in sas this would correspond to the repeated statement. In r u can use e.g. the geepack library for marginal modelling.

There are also other ways to deal with and investigate communities between variables, such as factor analysis and calculating cronbachs alpha for constructs.

Best of luck with your analysis! Carl

If your assumption is that there is dependence between the different questions, ie that they are associated with the same "construct" then it makes beautiful sense to use repeated measures modelling.

I would make sure in a first step to make sure to transform the variables to the same direction and size:

• you may have completely different variance and offset, so I would centre and scale the variables first.
• anxiety and contentment are obviously going in opposite direction. So after catering and scaling it would make sense to multiply opposing variables by -1.

After that you can perform a rm model either using individual as random factor or by marginal modelling (in sas this would correspond to the repeated statement. In r u can use e.g. the geepack library for marginal modelling.

There are also other ways to deal with and investigate commonality between variables, such as factor analysis and calculating cronbachs alpha for constructs.

Best of luck with your analysis! Carl