Hi Gene,
Hox outlines this strategy often in proximity to a discussion about centering, which can influence coefficients and the interpretation thereof. Not sure what you’re after, but Enders & Tofighi (2007) have an excellent and thorough article on centering, the implications, and how to interpret results under various strategies.
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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