Hi all --
Again -- I'm jumping on the band wagon in support
of these messages that
advocate-- what I call -- a
PREDICTION/REGRESSION/LINEAR MODELS approach.
I was attracted to Lee Wilkinson and SYSTAT many
years ago when Lee
had a sign at one of his SYSTAT BOOTHS that
said:
"Ask me about Cell Means Analysis" (May not be
Lee's exact words)
I was so excited to see a software package
that required the user to
insert the word CONSTANT in the regression model
when the user
wanted it -- NOT AS THE DEFAULT. When using
SAS at
Clemson in 1985-86, I had to tell students that
they must use the NOINT
OPTION until I explained why. A most
misunderstood and troublesome idea
is the lack of understanding of the predictor, U,
a vector of 1's. If students
would -- in the beginning -- insert THEIR OWN U,
when needed, then they might
have a better understanding of the "efficiency" of
having the CONSTANT or INTERCEPT
as the DEFAULT. This lack of understanding about
the CONSTANT or INTERCEPT is
revealed by the many Email messages we see related
to "What is RSQ WHEN there is NO
CONSTANT or INTERCEPT".
It is interesting that the more "modern" versions
of SYSTAT require the user to
REMOVE THE CONSTANT when appropriate.
It would be really great if the statistics
education folks would advocate the
introduction of PREDICTION/REGRESSION/LINEAR
MODELS early so that the students
would have something useful in their experience
and perhaps continue their study
of statistics. I'm afraid that many FIRST
STATISTICS COURSES have little
"selling/marketing" effect on
students.
The "Cell-Means Approach" is easy to introduce to
high school students, since
these students have experiences with AVERAGES,
MEANS, GPAs. And the
"Missing Cells Problem?" is really not a
problem until the students are
told that some folks don't know what to do about
"Missing Cells".
Enough "preaching to the choir"!!
--Joe
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************************************************************************
* Joe Ward Health Careers High School *
* 167 East Arrowhead Dr 4646 Hamilton Wolfe *
* San Antonio, TX 78228-2402 San Antonio, TX 78229 *
* Phone: 210-433-6575 Phone: 210-617-5400 *
* Fax: 210-433-2828 Fax: 210-617-5423 *
* [EMAIL PROTECTED] *
* http://www.ijoa.org/joeward/wardindex.html *
************************************************************************
----- Original Message -----
From: Gregory C. Mayer <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Tuesday, February 29, 2000 6:46
AM
Subject: Re: Howto interpret interactions in an
ANOVA
| anova, ancova and regression (and in their shorter Introduction to
| Biostatistics, anova and regression). They introduce them in turn,
| however; I agree that a text that began with glm and then took up anova,
| ancova and regression as instances of the general approach would be
| preferable. This is especially so when using Systat, as the model
| statements closely parallel the models, allowing more complex
| models to be grasped and implemented immediately, instead of being treated
| as some new technique.
|
| Gregory C. Mayer
| [EMAIL PROTECTED]
|
|
|
|
| On Mon, 28 Feb 2000, Bob Madden wrote:
|
| > I agree. In fact, I have sought in vain for an introductory level statistics
| > text that does not treat ANOVA and regression as two totally separate,
| > disconnected techniques.
| > With disconcerting monotony, they all monkey each other in this respect. I
| > think students
| > would be better served by being shown early on that regression, ANOVA, and for
| > that
| > matter, ANCOVA, are all special cases of the glm.
| >
| > --Bob Madden
| >
| > James Friedrich wrote:
| >
| > > Let me ad to the speculation regarding why interaction effects are often
| > > omitted from multiple regression. I think the reality is that people are
| > > generally trained in one "mode" or the other (ANOVA or Regression) without
| > > a sense of their connectedness (a point already alluded to in previoous
| > > posts). In an in-press national survey of undergraduate statistical
| > > instruction for psychology majors, I found that ANOVA dominates, with
| > > little attention to regression (except "simple"). The specialties of
| > > those teaching the stats / methods courses tends to be in laboratory -
| > > experimental areas where ANOVAs are the norm. The bottom line is that i
| > > don't think budding psychologists, at least, get much training - or good
| > > training - in MR or GLM perspectives. I also see this in advising /
| > > consulting I do with biology students. Sadly, I think the heavy ANOVA
| > > emphasis and minimal attention to regression approaches has the side
| > > effect of leaving people poorly schooled in measurement issues. My
| > > experience has been that professionals well-versed in MR / GLM are much
| > > more in tune with these concerns.
| > >
| > > By the way, I believe Stephen West and some colleagues have an entire book
| > > devoted to interaction effect analysis in multiple regression - a nice
| > > companion to Robert Rosenthal and colleagues book and chapters on the
| > > proper interpreation of ANOVA interactions.
| > >
| > > Jim Friedrich
| > > Willamette University
| >
|
|
|
