Paul R. Swank [EMAIL PROTECTED] wrote:
: Some years ago I did a simulation on the pretest-posttest control group
: design lokking at three methods of analysis, ANCOVA, repeated measures
: ANOVA, and treatment by block factorial ANOVA (blocking on the pretest using
: a median split).
I found that
Bruce Weaver [EMAIL PROTECTED] wrote:
: Paul's post reminded me of something I read in Keppel's Design and
: Analysis. Here's an excerpt from my notes on ANCOVA:
: the analysis of covariance is more precise with correlations greater than
: .6. Since we rarely obtain correlations
and
Analysis. Here's an excerpt from my notes on ANCOVA:
Keppel (1982, p. 512) says:
If the choice is between blocking and the analysis of covariance, Feldt
(1958) has shown that blocking is more precise when the correlation
between the covariate and the dependent variable is less than .4, while
Hi
On 26 Sep 2001, Burke Johnson wrote:
R Pretest Treatment Posttest
R PretestControl Posttest
In the social sciences (e.g., see Pedhazur's popular
regression text), the most popular analysis seems to be to
run a GLM (this version is often called an ANCOVA), where Y
is the
To: [EMAIL PROTECTED]
Subject: Re: Analysis of covariance
Hi
On 26 Sep 2001, Burke Johnson wrote:
R Pretest Treatment Posttest
R PretestControl Posttest
In the social sciences (e.g., see Pedhazur's popular
regression text), the most popular analysis seems to be to
run a GLM
I would have to respectfully disagree with Dennis' comment
also. Having the pre values twice in the model does not
hurt or change anything in interpreting the treatment effect.
BUT I do not like this approach. It makes the results more
difficult to interpret when you do have a variable in both
From my understanding, there are three popular ways to analyze the following design
(let's call it the pretest-posttest control-group design):
R Pretest Treatment Posttest
R PretestControl Posttest
In the social sciences (e.g., see Pedhazur's popular regression text), the most
At 02:26 PM 9/26/01 -0500, Burke Johnson wrote:
From my understanding, there are three popular ways to analyze the
following design (let's call it the pretest-posttest control-group design):
R Pretest Treatment Posttest
R PretestControl Posttest
if random assignment has
HI all,
I have to analyse some clinical data. In particular the analysis is a
comparison between two groups of the mean change baseline to endpoint of a
score. The statistician who planned the analysis used the ANCOVA on the mean
change, using as covariate the baseline values of the scores.
Do
At 10:26 AM 9/25/01 +, Morelli Paolo wrote:
HI all,
I have to analyse some clinical data. In particular the analysis is a
comparison between two groups of the mean change baseline to endpoint of a
score. The statistician who planned the analysis used the ANCOVA on the mean
change, using as
If you are using ANCOVA then the base score is the covariate and the final
score the criterion. ANCOVA is generally preferred to ANOVA on gain scores.
John Ambrose
University of the Virgin Islands
St. Thomas VI 00802
At 10:26 AM 9/25/01 +, Morelli Paolo wrote:
HI all,
I have to analyse
Morelli Paolo wrote:
I have to analyse some clinical data. In particular the analysis is a
comparison between two groups of the mean change baseline to endpoint of a
score. The statistician who planned the analysis used the ANCOVA on the mean
change, using as covariate the baseline values of the
At 03:19 PM 9/25/01 +, Radford Neal wrote:
Neither the question nor the response are all that clearly phrased, but
when I interpret them according to my reading, I don't agree. For instance,
if you're measuring pain levels, I don't see anything wrong with measuring
pain before treatment,
In article [EMAIL PROTECTED],
Dennis Roberts [EMAIL PROTECTED] wrote:
the basic idea is to be able to explain the post score variance in terms
of something ELSE ... that is, for example ... we know that some of the
variance in pain is due to one's TOLERANCE for PAIN ... thus, if we can
remove
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