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List:       r-sig-mixed-models
Subject:    Re: [R-sig-ME]  Different p_values from between groups to within groups
From:       Thierry Onkelinx <thierry.onkelinx () inbo ! be>
Date:       2018-06-12 12:10:23
Message-ID: CAJuCY5wsnhnUMBy903__7gH5cZEC-8Zf+qnaJZwA-bRr2ym-JA () mail ! gmail ! com
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Dear Luca,

Those p-values are conditional on the model and not to be used for
model comparison. First find out which model is the most appropriate,
then to the post hoc tests.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
AND FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx@inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///////////////////////////////////////////////////////////////////////////////////////////
 To call in the statistician after the experiment is done may be no
more than asking him to perform a post-mortem examination: he may be
able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given body
of data. ~ John Tukey
///////////////////////////////////////////////////////////////////////////////////////////





2018-06-08 15:17 GMT+02:00 Luca Danieli <mr.lucedan@hotmail.it>:
> Hello everybody,
> 
> may I ask you a suggestion on how to interpret a weird result I have?
> 
> I have 3 groups (ExpertiseType), and through the use of contrast hypotheses, the \
> first model gives me this output: 
> model = lmer(Score~Closure*ExpertiseType+(1|Participant)+(1|Item), database, \
> REML=TRUE) Pr(>|t|)
> Closurecl_c1:ExpertiseTypeexp_c1    0.42203
> Closurecl_c2:ExpertiseTypeexp_c1    0.00601 **
> Closurecl_c3:ExpertiseTypeexp_c1   9.32e-08 ***
> 
> Another, more detailed model, gives me the following:
> 
> model = lmer(Score~Closure*ExpertiseType+ExpertiseType*LastPosition+Closure*LastPosition+(1|Participant)+(1|Item), \
> database, REML=TRUE) 
> Pr(>|t|)
> Closurecl_c1:ExpertiseTypeexp_c1    0.50738
> Closurecl_c2:ExpertiseTypeexp_c1    0.01059 *
> Closurecl_c3:ExpertiseTypeexp_c1   4.05e-08 ***
> 
> As you can notice, I have an interaction in both analyses, but if I look for the \
> same contrast hypotheses within the group for which I have the interaction, the \
> Closurecl_c2 effect disappeares. 
> model = lmer(Score~Closure*LastPosition+(1|Participant)+(1|Item), subset(database, \
> ExpertiseType==3), REML=TRUE) 
> Pr(>|t|)
> Closurecl_c1      0.4411
> Closurecl_c2      0.1419
> Closurecl_c3   5.00e-07 ***
> 
> Which one should I consider the most reliable output?
> Or, alternatively, what does this difference mean? I really don't know how to \
> interpret this outcome. I was expecting that within groups, the analysis would get \
> more defined. 
> Best
> Luca
> 
> 
> 
> [[alternative HTML version deleted]]
> 
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

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