Re: Why ANCOVA?
- From: sangdonlee@xxxxxxxxx
- Date: Wed, 5 Mar 2008 10:01:49 -0800 (PST)
2. ANCOVA analyses for each continuous test score (36 separate
ANCOVAs) to identify significant differences between patients in
Treatment 1 vs. Treatment 2, while controlling for 3 categorical
demographic variables.
I don't have that much experience in ANCOVA, but I thought this is
used to control for continuous "covariates", or does it apply to
categorical ones too? Would a multiple linear regression in this case
be enough to control for the significant categorical variables
identified by the chi-square analyses?
Your experiments did not "control" the effects of race, gender, and
age. In other words, your experiments did not assign samples
"randomly". For example, the people in the first treatment group are
much older than the people in the second group, the outcomes of your
experiments will be affected by "age". If you just do a t-test
(without knowing the age difference in the treatment groups), you may
conclude that there are treatment effects between the two groups. Are
they? Another example is the females in the first group exercise
regularly and eat vegetables a lot (but you did not know) while the
males in the second group are overweight (but you did not know), you
may conclude that there are gender differences.
Ahh, the endless questions of "Did you control this or that?" in
"OBSERVATIONAL" studies.
The KILLER in OBSERVATIONAL studies in understanding the cause-and-
effect relationship is the "collinearity" as the number of covariates
get larger. If two covariates are highly correlated, you can NOT
seperate the effects of the two covariates as well as the treatment
effects.
Hope this helps.
Sangdon Lee, Ph.D.,
GM Tech. Center.
.
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