Re: dealing with confounders and residuals

Posted by David Marso on Oct 08, 2011; 1:36am
URL: http://spssx-discussion.165.s1.nabble.com/dealing-with-confounders-and-residuals-tp4881181p4881812.html

I suggest (as a start) you look into MANCOVA (you really have a multivariate problem here and I'll bet the MCV's are correlated.  Two stage analysis using PC's as predictors was archaic 20+ years ago when I was studying SEM models.  Do you have theoretical hypotheses supported by previous research or is this a fishing expedition?  You could do much more efficient and directed analyses if you were to apply the appropriate tools.

Ian Martin-2 wrote

I'm looking at the possible association between environmental contaminants (heavy metals and PCBs in human blood) and various menstrual cycle response variables in the study participants.  Obscuring these possible relationships are some confounding variables shown to influence the menstrual cycle dependent variables: age, caffeine consumption, alcohol consumption, etc.

I have  run a regression analysis with one of the menstrual cycle variables (dependent) and the suite of confounders, and then saved the residuals.  In a regression on the residuals  (dependent) with the blood contaminant variables, I get some interesting and significant regression coefficients and an overall significant regression model.   I should also mention that I've used PCA on the suite of contaminant concentrations to create 6 uncorrelated predictor variables.

Is this a reasonable approach, or is there a better way to block out the effects of the confounders to see the effects of contaminants on the dependent variables?

best regards,
Ian
____________________
Ian D. Martin, Ph.D.

Data Analysis &
Environmental Consulting
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