http://spssx-discussion.165.s1.nabble.com/Best-model-analytic-strategy-given-data-tp5720498.html
Hi Everyone!
I am writing my dissertation proposal, and my advisor would like me to outline my potential analytic strategy. I, however, am using a restricted secondary data set which I have not received yet and only have access to the survey itself. So, I can only guess from past publications using the data and what little information is on the website about missing data and so forth.
The data is cross-sectional with potentially around 800 respondents.
In general, I plan to test the following relationships:
racial discrimination (RD) and criminal justice injustices (CJI) --> delinquency
RD and CJI --> negative emotions and social bonds --> delinquency
Racial Socialization X (RD and CJI) --> negative emotions and social bonds --> delinquency
So, mediating effects and interaction effects....
Given that data comes from a general sample, the delinquency indicator is likely positively skewed. Furthermore, many of the IVs (which there are more than listed above) are likely correlated.
I had originally proposed to use SEM, but my advisor didn't like that idea because of the cross-sectional nature of the data. So, given the limits of the DV, I decided I could recode it from a count of "have you ever done (a given delinquent act)" to an ordered categorical variable of non-offender, one-time offender, and repeat offender. Doing this I proposed to used ordered logit, which also allows for multicollinearity. He didn't like me recoding the DV and wants it to remain a count....
So, I would appreciate any advice on what analytical strategy to propose. I don't want to do OLS. But, what else can I do? Also, if I do go that route, I would need to log the DV, correct?
Again, any advice pointing me towards a more sophisticated strategy than OLS, yet still simplified, would be greatly appreciated.
Thanks!! Have a great weekend!
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~Deena