http://spssx-discussion.165.s1.nabble.com/Z-scores-tp5052939p5053410.html
As several other regular contributors have mentioned, standardizing
unrelated variables is seldom a good idea. - I like to standardize arbitrary
composite scores to M=50, SD=10, so that subgroup deviations are easily
noticed and described, without using decimal places. - Of course, SPSS
can "properly" create z-scores by subtracting the mean and dividing by
the SD.
Presumably, you want to see on sense of the "relative contributions"
of the variables. That is always problematic for what are properly
called "partial regression coefficients" - Either you get a the same
ordering from standardized coefficients as from the simple p-values;
or other results frankly warn that the predictor variables are confounding
each other, so that the notion of "unique contribution" is a misnomer.
So you can either use the p-values, while recognizing their weaknesses,
or write your story from knowledge of how the variables have to interact.
OLS regression has always provided standardized regression coefficients
(criterion standardized, too), and the main use that I have ever found
for them is a warning of suppressor relationships. Suppressor relations,
where the difference of related variables is more important than their sum,
happen almost as readily (this introduces the topic of artifacts) in Logistic
as in Ordinary Least Squares.
--
Rich Ulrich
Date: Tue, 6 Dec 2011 17:52:19 +0000
From:
[hidden email]Subject: Z scores
To:
[hidden email]
Dear List,
I am running a binary logistic regression and I wanted to standardise the predictors beforehand by converting to z scores so that the regression coefficients in the output are standardised. However, some of these predictors are already standardised with a mean of 100 and SD of 15. Will SPSS convert these standard scores to z scores properly?
Kathryn