Dear R users and experts,

I tried to fit a model as shown below:

data.lmer<-lmer(word1~compoundType*nativelanguage + (1|subject) + (1|word),
data=data, family="binomial").

the factor 'compoundType' has three levels: blue, green, red.
the factor nativelanguage has two levels: english, other

I obtained the following results:
************************************************
Fixed effects:
                              Estimate Std. Error z value Pr(>|z|)
(Intercept)                    -1.0190     0.5777  -1.764   0.0778 .
nativelanguageother            -0.6862     0.4075  -1.684   0.0922 .
typegreen                      19.7882   728.0627   0.027   0.9783
typered                         3.6985     0.8359   4.425 9.65e-06 ***
nativelanguageother:typegreen -16.1915   728.0624  -0.022   0.9823
nativelanguageother:typered    -2.8106     0.5639  -4.984 6.23e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
************************************************

As you can see, the standard errors and estimates for "typegreen" as well as
"nativelanguageother:typegreen" are enormous. I examined my data and
realized that the responses obtained for the level "green" of compoundType
were all positive. But the responses of "green" were clearly significantly
different from the base level "blue" as green was 100% positive responses,
whereas blue had only 30% of positive responses.

I wonder if there is any way to conduct statistical analyses to show that
green is significantly different from blue (and potentially red) because
obviously I can't just say they are different :)...

Thank you in advance for your help!



Best,

Xiao He
Graduate student
Department of Linguistics
University of Southern California

	[[alternative HTML version deleted]]


