Date:         Tue, 29 Jul 2008 15:35:33 -0400
Reply-To:     Arthur Kramer <akramer@NJCU.edu>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Arthur Kramer <akramer@NJCU.edu>
Subject:      Re: Multiple Regression Models
Comments: To: "Swank, Paul R" <Paul.R.Swank@uth.tmc.edu>
In-Reply-To:  <6250203B042D8349A920AA6103830936060B59A2@UTHEVS3.mail.uthouston.edu>
Content-type: text/plain; charset=us-ascii

True enough. However, in an applied situation one may have to decide if the
cost of keeping a variable is worth the cost--financial or otherwise--of
inclusion. Parsimony may take precedence.

Arthur Kramer, Ph.D.
Director of Institutional Research
New Jersey City University
Phone: 201-200-3073
Fax: 201-200-3288

"...believe half of what you see and none of what you hear."
                  N.Whitfield and B.Strong

-----Original Message-----
From: Swank, Paul R [mailto:Paul.R.Swank@uth.tmc.edu]
Sent: Tuesday, July 29, 2008 12:06 PM
To: Arthur Kramer; SPSSX-L@LISTSERV.UGA.EDU
Subject: RE: Multiple Regression Models

If I have a significant variable in a model, I try to understand why
it's there before I discard it just because it doesn't fit my theory.
Sometimes serendipity happens.

Paul R. Swank, Ph.D.
Professor and Director of Research
Children's Learning Institute
University of Texas Health Science Center - Houston


-----Original Message-----
From: Arthur Kramer [mailto:akramer@NJCU.edu]
Sent: Tuesday, July 29, 2008 7:56 AM
To: Swank, Paul R; SPSSX-L@LISTSERV.UGA.EDU
Subject: RE: Multiple Regression Models

I think I would look at the "R^2 change" of this variable in the
complete
model, and then consider the cost/benefit of keeping this model in
regard to
my theory and what I would be using the model to support.

Arthur Kramer, Ph.D.
Director of Institutional Research
New Jersey City University
Phone: 201-200-3073
Fax: 201-200-3288

"...believe half of what you see and none of what you hear."
                  N.Whitfield and B.Strong
-----Original Message-----
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of
Swank, Paul R
Sent: Monday, July 28, 2008 5:42 PM
To: SPSSX-L@LISTSERV.UGA.EDU
Subject: Re: Multiple Regression Models

The question is, is the increase significant. If so then there are at
least two possible reasons. One is power. If the other variables in the
model account for a large proportion of the DVs variance, then this
variable may become significant because of the increased sensitivity of
the F test when the overall R^2 is larger. The second possibility is a
suppressor variable. That is, this variable is unrelated to the DV but
is related to another IV and suppresses superfluous variance in the
other IV and making the model seem stronger.

Paul R. Swank, Ph.D.
Professor and Director of Research
Children's Learning Institute
University of Texas Health Science Center - Houston

-----Original Message-----
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of
jimjohn
Sent: Monday, July 28, 2008 3:21 PM
To: SPSSX-L@LISTSERV.UGA.EDU
Subject: Multiple Regression Models

when you guys are building multiple regression models, if an independent
variable on its own is not significant, but in a regression model with
many
other independent variables, adding this variable increases the strength
of
the model (higher Adjusted R^2). In this case, would you recommend
keeping
the model with the variable that on its own is not significant. Or,
would
you only consider variables that have significatn pairwise correlations
with
the dependent variable. Thanks!
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