Date:         Mon, 19 May 2008 13:51:34 -0500
Reply-To:     "Swank, Paul R" <Paul.R.Swank@uth.tmc.edu>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         "Swank, Paul R" <Paul.R.Swank@uth.tmc.edu>
Subject:      Re: regression - nonsig to sign
Comments: To: "Zdaniuk, Bozena" <bozena@pitt.edu>
In-Reply-To:  <C9563A8511C9BB488B41E541ACC749F95E85C5A4A9@PITT-EXCH-07.univ.pitt.edu>
Content-Type: text/plain; charset="us-ascii"

There are two ways in which this can happen. First and most commonly, it
is as Hector has said, the variables B & C relate to variability in the
dependent variable (call it Y) that is not associated with A. Ten the
effect of A stands out more once this other variance is removed. This is
the reason we often have covariates in our models, the control for other
sources of variation in Y. So we increase the power of our test on A.
The other was is when B &/or C are related to A and reduce the
extraneous variance in A that is not related to Y. This is often called
a suppressor effect. Thus, it would help to look at the whole
correlation matrix of A, B, C and Y to see how these variables
interrelate.

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
Zdaniuk, Bozena
Sent: Monday, May 19, 2008 8:35 AM
To: SPSSX-L@LISTSERV.UGA.EDU
Subject: regression - nonsig to sign

Hello, when I enter predictor A by itself into regression model, it is
not significant. But if I add predictors B and C, predictor A becomes
significant. How can I interpret it?
thanks a lot.
Bozena

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