Gene,
Raudenbush standardizes the level 2 predictor variables on the variance between level 2 units in the y variable, reasoning that level 2 variables predict the between-unit variance, rather than the within-unit variance. There is a pretty extensive “worked example” of this in Raudenbush et al (1992) On the job improvements in teacher competence, which can be accessed on Google Books. For your purposes, you would need the sd of the level 1 intercept.
Best,
Steve
www.StatisticsDoc.com
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Gene Maguin
Sent: Wednesday, January 04, 2012 9:51 AM
To: [hidden email]
Subject: Re: standardized values in multilevel
Thanks, for all the replies to my request (and the citation). I’ll do the standardized analysis if I need to but what I was hoping for was what Ryan posted. However, here is the point I’m not sure about. In the model were x is the level 2 predictor of variation of the y intercept
Mixed y with x/fixed x/. . . .
I can get sd of x from the univariate stats. However, for sd of y do I want the univariate stats value of y, as would be true in an ordinary one level regression, or do I need the sd of the level 1 intercept? And, if I need the sd of the level 1 intercept, how can I get that from mixed?
Thanks, Gene Maguin
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of R B
Sent: Tuesday, January 03, 2012 8:12 PM
To: [hidden email]
Subject: Re: standardized values in multilevel
Gene,
A simple approach would be to fit the linear mixed model using the original variables and then to apply the following formula:
std coeff = [(unstd coeff) * (sd of x)] / (sd of y)
You could enter "standardized" variables into the linear mixed model, but keep in mind that the variance components will likely change.
The formula above, along with a detailed discussion, can be found in "Multilevel Analysis: techniques and applications" by J.J. Hox.
HTH,
Ryan
On Tue, Jan 3, 2012 at 11:27 AM, Gene Maguin <[hidden email]> wrote:
I know this is a controversial request because I have seen Cam's (and
other's) citations on both Multilevel and Semnet on this topic and I don't
want to have that discussion. I'd like a response to the technical question
of whether (and how) standardized values of fixed effects can be computed
given spss (I may have missed it but I don't think mixed can output those
values). If there is a computational citation, that would be fine.
Concretely, given
Mixed f with b c/fixed b c/print solution/random intercept | subject(xx)
covtype(id).
I get unstandardized regression coefficients for b and c. How do I
standardize them? I know this is possible in mplus. I'd like to do it in
spss.
Thanks, Gene Maguin
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