11 September 2019 3 10K Report

Hello there,

I have been struggling to wrap my head around which statistical analysis to use for my research question:

I have 17-20 potential outcome variables (subtests on a cognitive battery, haven't decided yet whether I will retain three of them or not).

I have three predictor variables, age, gender and pain. Pain is my primary predictor variable of interest. I would like to know how pain can predict cognition differentially.

I built the model on AMOS (picture included... yikes) and it is saturated.

But because it's not a path per-say, would global fit matter? Can I pursue this analysis regardless and start interpreting the local fit (which paths are significant?) Since the outcome variables are not exogenous. Should I even report global fit?

Further to this, would I report the b, se b, ß, CR and p for each path? And R-squared for each variable?

And finally, is there any way to conduct a hierarchical multivariate multiple regression? Ideally, I would like this to be hierarchical, since I want to know pain's unique variance and adj. R2, but as far as I can see, there's no way I can do that wish so many outcome variables.

The covariances were added by my supervisor because they are all measuring cognition, so it followed that they would all be correlated with one another.

ANY HELP! would be so, so, so, greatly appreciated.

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