If you are worried about collinearity, don't look at correlations. Look
at a collinearity diagnostic such as Belsley's Condition Index; for
details see Belsley, DA Conditioning Diagnostics.
Peter L. Flom, PhD
Assistant Director, Statistics and Data Analysis Core
Center for Drug Use and HIV Research
National Development and Research Institutes
71 W. 23rd St
New York, NY 10010
(212) 845-4485 (voice)
(917) 438-0894 (fax)
>>> "Shayna Padgett" <shayna@knology.net> 08/24/02 06:52AM >>>
Hi. I am a Masters student preparing to defend my thesis. I have come
up with a problem I am not sure how to address. I am performing logistic
regression on my data based on a dichotomous disease outcome (yes/no). I
am worried that there might be a correlation between two of my variables
(one is nights awake per month, and the other is use of a supplemental
hormome). I did a Point Beserial Correlation and got and R of 0.15, that
was signficant. This was sucha small number, I assumed the correlation
was weak. However, it was then suggested to me to do a simple t-test
comparing the mean of nights for the two yes/no groups and doing so, I
get significant differences, with one group having a mean twice that of
the other, which to me implies correlation. Do you know the best way to
sort this out? I am unsure which is most correct. Thanks.
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