[R] Major difference in the outcome between SPSS and R statisticalprograms

Doran, Harold HDoran at air.org
Fri Aug 1 17:35:42 CEST 2008


The biggest problem is that SPSS cannot fit a generalized linear mixed
model but lmer does. So, why would you expect the GLM in SPSS and the
GLMM in lmer to match anyhow? 

> -----Original Message-----
> From: r-help-bounces at r-project.org 
> [mailto:r-help-bounces at r-project.org] On Behalf Of Draga, R.
> Sent: Friday, August 01, 2008 10:19 AM
> To: r-help at r-project.org
> Subject: [R] Major difference in the outcome between SPSS and 
> R statisticalprograms
> 
> Dear collegues,
>  
> I have used R statistical program, package 'lmer', several 
> times already.
> I never encountered major differences in the outcome between 
> SPSS and R.
> ...untill my last analyses.
>  
> Would some know were the huge differences come from.
>  
> Thanks in advance, Ronald
>  
> In SPSS the Pearson correlation between variable 1 and 
> variable 2 is 31% p<0.001.
> 
>  
> 
> In SPSS binary logistic regression gives us an OR=4.9 (95% CI 
> 2.7-9.0), p<0.001, n=338.
> 
>                     OR          lower   upper
> 
> gender          1,120       0,565   2,221
> 
> age               0,985       0,956   1,015
> 
> variable 2         4,937   2,698   9,032
> 
>  
> 
> In R multilevel logistic regression using statistical package 'lmer'
> gives us an OR=10.2 (95% CI 6.3-14), p=0.24, n=338, groups: 
> group 1, 98; group 2 84.
> 
>                    OR           lower   upper
> 
> gender         2,295        -2,840 7,430
> 
> age              0,003        -70,047           70,054
> 
> variable 2     10,176     6,295   14,056
> 
>  
> 
> The crosstabs gives us:
> 
>      variable A
> 
> Var B  0   1
> 
>     0 156 108
> 
>     1  17  57
> 
>  
> 
> Would somebody know how it is possible that in SPSS we get 
> p<0.001 and in R we get p=0.24?
> 
> 
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> 
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