The simple answer to your question is that you use race as a
grouping variable and you test for the whether the effect is the
same across levels of race, that is, a nonsignificant two-way
interaction -- assuming you have sufficient statistical power to
detect such an interaction. If you have a significant two-way
interaction, that is, effects are different for the two groups,
then you can not come up with a result that applies
to all persons independent of race -- the effect is dependent
upon the race of the person. If the effect is nonsignificant
with adequate power, then the effect holds for the races
represented in the design.
Given the sample sizes you list below, I think descriptive
analyses are more appropriate than inferential analyses but
what the hell, who knows, maybe N=2 does provide sufficient
information for statistical testing. Knock yourself out.
-Mike Palij
New York University
[hidden email]On Sat, Sep 29, 2012 at 8:32 PM, Justin Blehar <
[hidden email]> 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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