Best model/analytic strategy given data?
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Best model/analytic strategy given data?
 
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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! -- ~Deena |
Automatic reply: Best model/analytic strategy given data?
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Hi,
I'm away on leave, and will return to work on Monday 10th June. I will not have regular access to my email account while away, and will endeavour to return your emails shortly after my return. Cheers, Matt. |
Re: Best model/analytic strategy given data?
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In reply to this post by Deena Isom
Deena, It seems that your chair has already set limits to what you can be allowed to do. Given those, I doubt that anything that anybody on this listserv or the semnet listserv will change her/his limits. I think your problem is small ‘p’ political rather than statistical. I hope that you have a statistics/methodology person in your department/school or an ‘outside’ such person who might be respected by your chair and who you can work with to persuade your chair to give up his/her preconceived and incorrect ideas. Good luck. Gene Maguin From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Deena Isom 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! |
Re: Best model/analytic strategy given data?
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Gene (and Deena),
SEM is not simple. It certainly is not a magical way to make good sense of data that are not readily modeled in simpler terms. Rather, it is a sophisticated presentation of a whole set of inter-relationships. I suspect that the advisor is giving excellent and proper advice when he rejects SEM for *this* set of proposed predictors and mediators. Cross-sectional? How to you differentiate cause versus effect? And: How do you "operationalize" constructs like "social bonds" and "criminal justice injustices" on a subject-level? Finally, I wonder if there is going to be enough information -- enough delinquency -- in any "general sample" of 800. Positively skewed, yes. Are there more that 50 cases that are not zero? Off-hand, recoding to (0, 1, 2+) seems like not-a-bad-idea, but what-to-look-at also depends on what the observed distribution happens to be. -- Rich Ulrich Date: Fri, 31 May 2013 11:27:00 -0400 From: [hidden email] Subject: Re: Best model/analytic strategy given data? To: [hidden email] Deena, It seems that your chair has already set limits to what you can be allowed to do. Given those, I doubt that anything that anybody on this listserv or the semnet listserv will change her/his limits. I think your problem is small ‘p’ political rather than statistical. I hope that you have a statistics/methodology person in your department/school or an ‘outside’ such person who might be respected by your chair and who you can work with to persuade your chair to give up his/her preconceived and incorrect ideas. Good luck. Gene Maguin
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Deena Isom
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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