On Fri, 22 Feb 2008 02:01:35 -0800 (PST), Jean K <cjkuo584@[EMAIL PROTECTED]
>
wrote:
> Hi Group,
>
> I need to repeat an analysis that was conducted on a smaller dataset,
> but after looking at the previous statistician's methods, I'm a little
> confused at why she chose to use ANCOVA. Any insight would be
> appreciated!
>
> Response Variable: Scores on 36 different Tests
> Main Predictor of Interest is Treatment Type (1 or 2)
> Data was collected from different individuals in each treatment group.
>
> What she did:
> 1. Chi-square tests between Treatment Type and Clinical/Demographic
> Factors (i.e. Race, ***, Age) and identified 3 significant variables.
If Age was measured as continuous, then collapsing
it into categories for doing contingency tests is a waste
of power.
>
> 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?
ANOVA can be regarded as a specific arrangement of
terms and tests which can all be obtained from OLS
regression. ANOVA "factors" with more than two levels
need to be coded into k-1 dummy variables, for k levels,
and "ANOVA" computer programs typically do all that for
you. To use a REGRESSION package, you might have
to construct the dummies as new variables to use
regression for more than two levels of categories.
ANCOVA is ANOVA whose overt layout has an increased
resemblance to regression. You do not get "counts" or
means for the subsidiary statistics for the covariates, which
would be available if those variables were included as factors.
The design you describe will give exactly the same result
as the regression, or as the ANOVA with 4 factors that
does not look at any interaction terms -- assuming that
the analyses are done correctly and elect similar options.
--
Rich Ulrich
http://www.pitt.edu/~wpilib/index.html
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