On Jul 19, 2011, at 7:19 PM, J. wrote:
@Dimitri: I tried to enter it as numeric and still got the same
outcome. I
still wonder if there is any way to get the same result from both
programs.
@David, Bert: Yes, I found that the gender coefficient is R is
exactly twice
that of the one from SPSS. Need to study on parametrization.
Yes. I tested my own advice and did a google search with "different
parametrization of dummy variables spss and r".
1)
http://support.spss.com/productsext/spss/documentation/statistics/articles/catreg3.htm
2)
http://www.thejuliagroup.com/blog/?p=1531
3)
I'm not sure it should be in a publicly accessible site, but Google
links to a pdf of the full text of "Data Analysis and Graphics Using R
– an Example-Based Approach: Third Edition" by Maindonald % Braun
http://lib.dnu.dp.ua:8001/l/%D0%9A%D0%BE%D0%BF%D1%8C%D1%8E%D1%82%D0%B5%D1%80%D1%8B%D0%98%D1%81%D0%B5%D1%82%D0%B8/%D0%9F%D0%BE%D0%BF%D1%83%D0%BB%D1%8F%D1%80%D0%BD%D1%8B%D0%B5%20%D0%BF%D1%80%D0%BE%D0%B3%D1%80%D0%B0%D0%BC%D0%BC%D1%8B/S-PLUS%20R/Data%20Analysis%20and%20Graphics%20Using%20R%203rd%20Edition.pdf
And chapter 7 would be where to look.
Bottom line. You should only be looking at coefficient values when you
know the coding of your factors. You cannot interpret the coefficients
of an SPSS run as differences between males and females, because they
are based on a -1 vs. 1 coding, what in R are called sum.contrasts. R
uses a default of treatment contrasts (0 versus 1) but will offer
sum.contrasts if asked nicely. (And you should never interpret "main
effects" coefficients when you are using interactions in models.
Always use predictions in that instance.)
Thanks,
Jay
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