[R-sig-ME] Orthogonal vs. Non-orthogonal contrasts
thierry.onkelinx at inbo.be
Wed May 25 09:44:14 CEST 2016
A is part of two interactions. Hence you cannot interpret this main effect
without the interactions. Note that changing the contrast will also effect
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
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
2016-05-25 4:42 GMT+02:00 Yasuaki SHINOHARA <y.shinohara op aoni.waseda.jp>:
> Dear all,
> Hello, I am doing research of second language acquisition.
> I am wondering about the glmer in R for my analyses. Could you please
> answer my question?
> I have the following logistic mixed effects model.
> model<-glmer(corr ~ A + B + C + D + A:B + B:C + A:D +(1+A|subject) +
> I tested language learners (subjects) three time (pre-training,
> mid-training, post-training) with the "item" produced by "speaker", so
> Factor A is "testing block" which has three levels (i.e., pre, mid, post).
> Since each subject took the test three times, the random slopes for the
> Factor A were also included as a random factor.
> I made an orthogonal contrast for the Factor A (testing block) as follows.
> The results from summary(model) function for this factor were as follows.
> pre vs. mid test: β = 0.22, SE = 0.05, z = 4.34, p < 0.001
> pre & mid vs. post test: β = -0.21, SE = 0.04, z = -5.96, p < 0.001.
> However, I thought it would be better if I made a non-orthogonal contrast
> for this factor as "pre vs. mid" and "pre vs. post" to test my hypothesis.
> So I made a new contrast for the Factor A as follows.
> The results from summary(model) function for this contrast were
> pre vs. mid test: β = -0.01, SE = 0.04, z = -0.14, p > 0.05 (=0.89),
> pre vs. post test: β = 0.42, SE = 0.07, z = 5.96, p < 0.001.
> Although the first contrast (pre vs. mid) is the same for both models, why
> the results of pre vs. mid contrast are so different (one is very
> significant, but the other one is not significant)?
> I really appreciate any help.
> Best wishes,
> R-sig-mixed-models op r-project.org mailing list
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