Multicollinearity in Moderation Analysis with binary Moderator

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Multicollinearity in Moderation Analysis with binary Moderator

E. Bernardo

Dear SPSS Users,

I am conducting moderation analysis with binary moderator (M) and continuous observed (not latent) DV and IV.  In order to address a multicollinearity I use mean centering on the IV, then I I used hierarchical regression with the following steps:

1.  The IV was mean centered ( I called it IVcentered). 
2.  The IVCentered was multiplied with the M to get the product term (IVCentered*M)
3. A three step Hierarchical Linear Regression was conducted. 
3.1 STEP1: Regression model of DV on IVcentered
3.2 STEP2: Regression model of DV on IVCentered + M
3.3 STEP3: Regression model of DV on IVCentered + M + IVCentered*M

The problem arises was that the VIF are very high on IVcentered and IVCentered*M.  These VIF are greater than 13.
Can you suggest a solution on the multicollinearity? Or alternative approach?

Thank you.
Eins

          
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Re: Multicollinearity in Moderation Analysis with binary Moderator

Rich Ulrich
I expect that you have the binary moderator (M) coded as 0/1, which
would give the greatest size for the VIF; coding as 1/2 will be almost
as bad.

Is VIF okay if you code M as 1/ -1 ?

If not, then you have a strongly skewed moderator (mostly one value),
and you are probably stuck with those results.

--
Rich Ulrich


Date: Sun, 14 Dec 2014 04:43:01 +0000
From: [hidden email]
Subject: Multicollinearity in Moderation Analysis with binary Moderator
To: [hidden email]


Dear SPSS Users,

I am conducting moderation analysis with binary moderator (M) and continuous observed (not latent) DV and IV.  In order to address a multicollinearity I use mean centering on the IV, then I I used hierarchical regression with the following steps:

1.  The IV was mean centered ( I called it IVcentered). 
2.  The IVCentered was multiplied with the M to get the product term (IVCentered*M)
3. A three step Hierarchical Linear Regression was conducted. 
3.1 STEP1: Regression model of DV on IVcentered
3.2 STEP2: Regression model of DV on IVCentered + M
3.3 STEP3: Regression model of DV on IVCentered + M + IVCentered*M

The problem arises was that the VIF are very high on IVcentered and IVCentered*M.  These VIF are greater than 13.
Can you suggest a solution on the multicollinearity? Or alternative approach?

Thank you.
Eins

===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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Re: Multicollinearity in Moderation Analysis with binary Moderator

E. Bernardo
Dear Rich,

You are right that using 1/2 code is bad. The VIF reduces (down to its acceptable level) when I changed the codes from 1/2 to 0/1.
So, coding in binary moderator case matters a lot.

Thank you for your suggestion. I did not use anymore the +1/-1 codes.

Eins


On Sunday, December 14, 2014 3:12 PM, Rich Ulrich <[hidden email]> wrote:


I expect that you have the binary moderator (M) coded as 0/1, which
would give the greatest size for the VIF; coding as 1/2 will be almost
as bad.

Is VIF okay if you code M as 1/ -1 ?

If not, then you have a strongly skewed moderator (mostly one value),
and you are probably stuck with those results.

--
Rich Ulrich


Date: Sun, 14 Dec 2014 04:43:01 +0000
From: [hidden email]
Subject: Multicollinearity in Moderation Analysis with binary Moderator
To: [hidden email]


Dear SPSS Users,

I am conducting moderation analysis with binary moderator (M) and continuous observed (not latent) DV and IV.  In order to address a multicollinearity I use mean centering on the IV, then I I used hierarchical regression with the following steps:

1.  The IV was mean centered ( I called it IVcentered). 
2.  The IVCentered was multiplied with the M to get the product term (IVCentered*M)
3. A three step Hierarchical Linear Regression was conducted. 
3.1 STEP1: Regression model of DV on IVcentered
3.2 STEP2: Regression model of DV on IVCentered + M
3.3 STEP3: Regression model of DV on IVCentered + M + IVCentered*M

The problem arises was that the VIF are very high on IVcentered and IVCentered*M.  These VIF are greater than 13.
Can you suggest a solution on the multicollinearity? Or alternative approach?

Thank you.
Eins

===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD


===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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Re: Multicollinearity in Moderation Analysis with binary Moderator

Rich Ulrich
Coding with binary predictors especially matters when you compute
your own interactions:  And you still have not paid enough attention
to it.   Potentially (and what I stated), using 0/1 is worse than using
1/2:  Please do pay attention to the values that you can get when
you do the computation by hand.  And consider what happens when
0/1 is skewed, rather than being near 50%.

Using 0/1 has to drop information:  The other values are multiplied by
zero, after all.  If you have a small number of zero, then the interaction
term computed this way has identical to the other variable, except for
the few cases that are set to zero.   On the other extreme -- which you
may have, since it seemed an improvement -- a large number of zeros
means that you are only looking at a range of non-zero scores in the
interaction for the few people who were scored "1".  This score will have
a low VIF mainly because it contains such a small amount of information. 

The VIF between an interaction and its two component variables will go
to zero when you use mean-adjusted-to-zero scores for both variables. 
That is, if 90% of the sores are scored ones, average for 0 and 1 of 0.9,
you might score those as  "+0.1" and "-0.9"  by subtracting off the mean.


Looking at the actual scores is also the way to help consider effects of other
transformations, including (especially) rank-order transformations when
most scores are ties.

--
Rich Ulrich


Date: Sun, 14 Dec 2014 15:59:43 +0000
From: [hidden email]
Subject: Re: Multicollinearity in Moderation Analysis with binary Moderator
To: [hidden email]

Dear Rich,

You are right that using 1/2 code is bad. The VIF reduces (down to its acceptable level) when I changed the codes from 1/2 to 0/1.
So, coding in binary moderator case matters a lot.

Thank you for your suggestion. I did not use anymore the +1/-1 codes.

Eins


On Sunday, December 14, 2014 3:12 PM, Rich Ulrich <[hidden email]> wrote:


I expect that you have the binary moderator (M) coded as 0/1, which
would give the greatest size for the VIF; coding as 1/2 will be almost
as bad.

Is VIF okay if you code M as 1/ -1 ?

If not, then you have a strongly skewed moderator (mostly one value),
and you are probably stuck with those results.

--
Rich Ulrich


Date: Sun, 14 Dec 2014 04:43:01 +0000
From: [hidden email]
Subject: Multicollinearity in Moderation Analysis with binary Moderator
To: [hidden email]


Dear SPSS Users,

I am conducting moderation analysis with binary moderator (M) and continuous observed (not latent) DV and IV.  In order to address a multicollinearity I use mean centering on the IV, then I I used hierarchical regression with the following steps:

1.  The IV was mean centered ( I called it IVcentered). 
2.  The IVCentered was multiplied with the M to get the product term (IVCentered*M)
3. A three step Hierarchical Linear Regression was conducted. 
3.1 STEP1: Regression model of DV on IVcentered
3.2 STEP2: Regression model of DV on IVCentered + M
3.3 STEP3: Regression model of DV on IVCentered + M + IVCentered*M

The problem arises was that the VIF are very high on IVcentered and IVCentered*M.  These VIF are greater than 13.
Can you suggest a solution on the multicollinearity? Or alternative approach?

Thank you.
Eins

===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD


===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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Re: Multicollinearity in Moderation Analysis with binary Moderator

E. Bernardo
My moderator has equal respondents.


On Monday, December 15, 2014 6:13 AM, Rich Ulrich <[hidden email]> wrote:


Coding with binary predictors especially matters when you compute
your own interactions:  And you still have not paid enough attention
to it.   Potentially (and what I stated), using 0/1 is worse than using
1/2:  Please do pay attention to the values that you can get when
you do the computation by hand.  And consider what happens when
0/1 is skewed, rather than being near 50%.

Using 0/1 has to drop information:  The other values are multiplied by
zero, after all.  If you have a small number of zero, then the interaction
term computed this way has identical to the other variable, except for
the few cases that are set to zero.   On the other extreme -- which you
may have, since it seemed an improvement -- a large number of zeros
means that you are only looking at a range of non-zero scores in the
interaction for the few people who were scored "1".  This score will have
a low VIF mainly because it contains such a small amount of information. 

The VIF between an interaction and its two component variables will go
to zero when you use mean-adjusted-to-zero scores for both variables. 
That is, if 90% of the sores are scored ones, average for 0 and 1 of 0.9,
you might score those as  "+0.1" and "-0.9"  by subtracting off the mean.


Looking at the actual scores is also the way to help consider effects of other
transformations, including (especially) rank-order transformations when
most scores are ties.

--
Rich Ulrich


Date: Sun, 14 Dec 2014 15:59:43 +0000
From: [hidden email]
Subject: Re: Multicollinearity in Moderation Analysis with binary Moderator
To: [hidden email]

Dear Rich,

You are right that using 1/2 code is bad. The VIF reduces (down to its acceptable level) when I changed the codes from 1/2 to 0/1.
So, coding in binary moderator case matters a lot.

Thank you for your suggestion. I did not use anymore the +1/-1 codes.

Eins


On Sunday, December 14, 2014 3:12 PM, Rich Ulrich <[hidden email]> wrote:


I expect that you have the binary moderator (M) coded as 0/1, which
would give the greatest size for the VIF; coding as 1/2 will be almost
as bad.

Is VIF okay if you code M as 1/ -1 ?

If not, then you have a strongly skewed moderator (mostly one value),
and you are probably stuck with those results.

--
Rich Ulrich


Date: Sun, 14 Dec 2014 04:43:01 +0000
From: [hidden email]
Subject: Multicollinearity in Moderation Analysis with binary Moderator
To: [hidden email]


Dear SPSS Users,

I am conducting moderation analysis with binary moderator (M) and continuous observed (not latent) DV and IV.  In order to address a multicollinearity I use mean centering on the IV, then I I used hierarchical regression with the following steps:

1.  The IV was mean centered ( I called it IVcentered). 
2.  The IVCentered was multiplied with the M to get the product term (IVCentered*M)
3. A three step Hierarchical Linear Regression was conducted. 
3.1 STEP1: Regression model of DV on IVcentered
3.2 STEP2: Regression model of DV on IVCentered + M
3.3 STEP3: Regression model of DV on IVCentered + M + IVCentered*M

The problem arises was that the VIF are very high on IVcentered and IVCentered*M.  These VIF are greater than 13.
Can you suggest a solution on the multicollinearity? Or alternative approach?

Thank you.
Eins

===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD


===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD


===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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Re: Multicollinearity in Moderation Analysis with binary Moderator

Bruce Weaver
Administrator
In reply to this post by E. Bernardo
Take a look at this:

http://www.statisticalhorizons.com/multicollinearity


E. Bernardo wrote
Dear SPSS Users,
I am conducting moderation analysis with binary moderator (M) and continuous observed (not latent) DV and IV.  In order to address a multicollinearity I use mean centering on the IV, then I I used hierarchical regression with the following steps:
1.  The IV was mean centered ( I called it IVcentered). 2.  The IVCentered was multiplied with the M to get the product term (IVCentered*M)3. A three step Hierarchical Linear Regression was conducted. 3.1 STEP1: Regression model of DV on IVcentered3.2 STEP2: Regression model of DV on IVCentered + M3.3 STEP3: Regression model of DV on IVCentered + M + IVCentered*M
The problem arises was that the VIF are very high on IVcentered and IVCentered*M.  These VIF are greater than 13.Can you suggest a solution on the multicollinearity? Or alternative approach?
Thank you.Eins
          

=====================
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[hidden email] (not to SPSSX-L), with no body text except the
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Re: Multicollinearity in Moderation Analysis with binary Moderator

Art Kendall
In reply to this post by E. Bernardo
"equal respondents" ?
what is the construct behind the moderator?

Does the moderator represent a random assignment to treatment?

By any chance is the moderator a coarsening of a variable via a median split?
Art Kendall
Social Research Consultants
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Re: Multicollinearity in Moderation Analysis with binary Moderator

Martin Holt-3
Art

Do you know: has Marta stopped contributing to the list?

Regards,

Martin
 
Martin P. Holt

Freelance Medical Statistician and Quality Expert

[hidden email]

Persistence and Determination Alone are Omnipotent !

If you can't explain it simply, you don't understand it well enough.....Einstein



Linked In: https://www.linkedin.com/profile/edit?trk=nav_responsive_sub_nav_edit_profile


From: Art Kendall <[hidden email]>
To: [hidden email]
Sent: Monday, 15 December 2014, 20:09
Subject: Re: Multicollinearity in Moderation Analysis with binary Moderator

"equal respondents" ?
what is the construct behind the moderator?

Does the moderator represent a random assignment to treatment?

By any chance is the moderator a coarsening of a variable via a median
split?



-----
Art Kendall
Social Research Consultants
--
View this message in context: http://spssx-discussion.1045642.n5.nabble.com/Multicollinearity-in-Moderation-Analysis-with-binary-Moderator-tp5728177p5728189.html
Sent from the SPSSX Discussion mailing list archive at Nabble.com.




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===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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Re: Multicollinearity in Moderation Analysis with binary Moderator

David Marso
Administrator
Word has it that Marta has been seduced/abducted by the Dark Side (Stata).
Something regarding abominable SPSS licensing fees at her institution from some time ago ;-(
She does pop in now and then (when she feels her ears burning).
I suspect she may even pop in today sometime ;-)
---
Martin Holt-3 wrote
Art
Do you know: has Marta stopped contributing to the list?
Regards,
Martin Martin P. Holt
Freelance Medical Statistician and Quality Expert

[hidden email]

Persistence and Determination Alone are Omnipotent !
If you can't explain it simply, you don't understand it well enough.....Einstein



Linked In: https://www.linkedin.com/profile/edit?trk=nav_responsive_sub_nav_edit_profile
 
      From: Art Kendall <[hidden email]>
 To: [hidden email] 
 Sent: Monday, 15 December 2014, 20:09
 Subject: Re: Multicollinearity in Moderation Analysis with binary Moderator
   
"equal respondents" ?
what is the construct behind the moderator?

Does the moderator represent a random assignment to treatment?

By any chance is the moderator a coarsening of a variable via a median
split?



-----
Art Kendall
Social Research Consultants
--
View this message in context: http://spssx-discussion.1045642.n5.nabble.com/Multicollinearity-in-Moderation-Analysis-with-binary-Moderator-tp5728177p5728189.html
Sent from the SPSSX Discussion mailing list archive at Nabble.com.



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