Posted by
Steve Simon, P.Mean Consulting on
Oct 01, 2012; 2:39pm
URL: http://spssx-discussion.165.s1.nabble.com/Controlling-for-Race-with-SPSS-20-tp5715386p5715403.html
Justin Blehar wrote:
> 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?).
"Controlling for Race" is more properly thought of as risk adjustment.
There are several ways to do this. The simplest is to fit a general
linear model with both race and smoking as independent variables. The
estimates for smoking that you get with the LSMEANS option represents
the estimated average outcome when the mix of race is the same in each
group. To do this well, you should create your own indicator variables
rather than let SPSS do it for you, as SPSS might choose the "wrong"
reference level.
Another way to do this, as you noted, is to restrict your sample to just
one race group, but this is wasteful of the data that you worked so hard
to collect. Still, that might be a nice secondary analysis.
The suggestion to look at interactions is not "controlling for race" but
rather, trying to identify subgroups. It might be, for example, that
smoking shows large differences in the Black subpopulation and not in
the Asian-American subpopulation. I generally discourage looking at
interactions, unless there is a strong a priori belief that
subpopulation effect exist, either based on previous research or based
on some plausible scientific mechanism.
Steve Simon,
[hidden email], Standard Disclaimer.
Sign up for the Monthly Mean, the newsletter that
dares to call itself average at www.pmean.com/news
=====================
To manage your subscription to SPSSX-L, send a message to
[hidden email] (not to SPSSX-L), with no body text except the
command. To leave the list, send the command
SIGNOFF SPSSX-L
For a list of commands to manage subscriptions, send the command
INFO REFCARD