I am working on a comparison of genders with regard to motivational constructs. I am hoping to get some advice as to how to best model the data.
1) Should I use your BEST software and run separate comparisons for each construct or use a hierarchical model?
2) What distribution should I use to describe the data? The constructs are measured for each student as averages of sets of questions on a survey using a five point Likert-type scale, so they can range from 1 to 5. The data are skewed toward lower values, but in a the handbook citing a validation study, some of the constructs had positive skewness.
3) Rather than using broad normal distributions, I’m thinking that the hyper-priors could be uniform ranging from 1 to 5 for the group means, and I could use uniform(0,2) for the group standard deviations since that would give equal credence across the range of possibilities for data that ranges from 1 to 5. Does that make sense?
I had an idea. For describing my data where each observation is an average for a set of Likert scales (1-5), would it make sense to subtract 1 and divide everything by four so that they are on a 0-1 scale and use a beta likelihood?