http://spssx-discussion.165.s1.nabble.com/Controlling-for-Race-with-SPSS-20-tp5715386p5715392.html
Pooling some of the groups would work, I'm thinking a combination of descriptive and inferential stats is probably going to be the best course of action. Yes current smokers comprise
both the heavy and light smokers so I am double counting cases. A 3 way design may work for this. My intent for the analysis is exploratory to gain a better understanding of how (if at all) smoking status impacts cognition in schizophrenia. The current literature
is all over the place with mixed results and various designs. I'm hoping that by using the higher number of groups it can reduce confounds and paint a clearer picture (e.g. they're may be no relation when looking at current smokers and processing speed but
a strong relation exists between heavy smokers and processing speed). Ideally I can look at each of these combinations between and within groups. All the while controlling for race.
Cognition is being assessed by: Vocabulary (VC), Matrix Reasoning(MR), Estimated IQ (from VC and MR scores). Ten other individual measures (TMT, BAC, HVLT, WMSlll, LNS, Mazes, BVMT, Fluency, MSCEIT, CPT) and raw scores are converted to T scores and then used
to make up 6 cognitive domains plus a total score (Processing Speed, Attention and Vigilance, Working Memory, Verbal Learning, Reasoning Speed, Social Cognition, and a Total Score for the Battery).
Thanks again and I appreciate your patience, I'm new to all of this and really enjoy learning from everyone!
V/R
Justin
From: Art Kendall [[hidden email]]
Sent: Sunday, September 30, 2012 8:31 AM
To: Justin Blehar
Cc: [hidden email]
Subject: Re: [SPSSX-L] Controlling for Race with SPSS 20
Since you have tiny Ns when grouping cases this way, perhaps you should pool some of the groups? Or use and additional IV? with a 3 way design
As is, it looks like you are double counting cases.
E.g., how do you distinguish heavy smokers from current smokers?
Can you put your cases into a 3 way design (3 * 2 *2), degree of exposure, race as IVs and cognition as the DV?
degree of exposure 3 levels: never, light, heavy
current status 2 levels: current/not
race 2 levels: Caucasian, African American.
How are you measuring cognition?
Art Kendall
Social Research Consultants
On 9/29/2012 8:32 PM, Justin Blehar wrote:
The population is outpatient individuals suffering from schizophrenia. Most of the research I found either does not list race or only has Caucasians in their sample. I have a large enough N to run only Caucasians but this limits how well the results can be generalized. I'm trying to avoid this if possible.
Total Sample
Caucasian - N = 76
African American- N = 44
Groups
Never Smokers: Caucasian N = 15 African American N = 23
Former Smokers: Caucasian N = 29 African American N = 2
Nonsmokers: Caucasian N = 44 African American N = 25
Heavy Smokers: Caucasian N = 9 African American N = 5
Light Smokers: Caucasian N = 23 African American N =14
Current Smokers: Caucasian N = 32 African American N = 19
Thanks for your reply :)
V/R
Justin
________________________________________
From: SPSSX(r) Discussion [[hidden email]] on behalf of Michael Palij [[hidden email]]
Sent: Saturday, September 29, 2012 8:11 PM
To: [hidden email]
Subject: Re: Controlling for Race with SPSS 20
If you control for race in whatever manner, to what population
would your conclusions apply to? And by population I mean
humans, not a mathematical distribution.
-Mike Palij
New York University
[hidden email]
On Sat, Sep 29, 2012 at 6:18 PM, Justin Blehar [hidden email] wrote:
Hello All,
Not sure how much detail is needed so I'll give you a quick overall but I'm
trying to control for race and am unsure how to best go about this. I know
that I can run a partial correlation and control for race using the menu but
is this really controlling for race? If not is there a better way? How would
I do this for a t-test?
This is a cross sectional design looking at cognition and smoking in a
psychiatric population. There are six groups I'm looking at; Never Smokers,
Former Smokers, Nonsmokers (includes both never smokers and former smokers),
Heavy Smokers, Light Smokers, and Smokers (includes heavy and light
smokers). I have 36 scale variables that I want to compare between each of
these groups. When I break out the groups by race (just looking at box plots
and mean comparisons) there are clearly some large race effects (e.g.
parental education, level of functioning, IQ, etc...). I'd like to be able
to correct for this in each analysis. I'm running both correlations and
t-tests (maybe this isn't the best process?).
If I run a partial correlation and control for race is this really
controlling for race?
When running the t-tests how do I control for race?
Any help would be greatly appreciated.
V/R
Justin
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