I am trying to get my masters thesis done. I am taking a statistics course right now, but I need to get started on things sooner rather than later.

Basically, I am looking at a treatment study data. Subjects underwent randomization to two conditions. Sample size is 153. I am trying to identify predictors of treatment outcome. I previously attempted to run logistic regression on the data, but I think I need to double check things before I move ahead. My questions are:

1. Can I do this in SPSS or AMOS? I worked with SPSS before, but not AMOS.

Should I use structural equation modelling?

2. My outcome variable is presence or absence of diagnosis at posttreatment, but also reduction in symtoms severity from pre to post. I am still not clear if logistic regression is the correct approach for diagnosis/no diagnosis as the outcome?

Another student I work with suggested DFA? Which technique is better?

For the reduction in symptoms from pre-post (measured based on self-report), I read a paper where the authors used reliable change indeces (RCs). How does one obtain RCs? I see from that articles that there is an interval to guide if RC is statistically significant. Then it looks as if I need to figure it if change is clinically significant? Some other studies regressed the predictors on the raw outcome score, which does not seem appropriate. How does one go about to regressing the predictors on the change indices? How will I split sample in improved/not improved based on RCs?

I am trying to figure this out on my own. I am taking a stats class now, but it will take some time to learn the answers to these and other questions, but I need to make progress on the thesis, so any help would be welcome.

Thank you.