[R-sig-ME] Orthogonal vs. Non-orthogonal contrasts

[Yasuaki SHINOHARA]

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) + 
(1+A|item:speaker),family=binomial,data=mydata,control=glmerControl(optimizer="bobyqa", 
optCtrl=list(maxfun=1000)))

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.
PreVsMid<-c(1,-1,0)
PreMidVsPost<-c(1,1,-2)
contrasts(mydata$A)<-cbind(PreVsMid,PreMidVsPost)

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.
PreVsMid<-c(1,-1,0)
PreVsPost<-c(1,0,-1)
contrasts(mydata$A)<-cbind(PreVsMid,PreVsPost)

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,
Yasu