Hi everyone, I'm having trouble with some 2-factor glmer models that I'm trying to run, such that the model with one of the main effects removed is coming out identical to the full model. Some colleagues suggested that this might be due to the coding of my factors, specifically because I have a factor that has 3 levels, and that one needs to be treated differently, but I'm not sure how - or why - to do that.

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Brief summary of my data: -My DV (called Target_E2_pref) is a binary categorical variable. -There are two categorical IVs: Listener (2 levels) and SyntaxType (3 levels). -Listener varies by both subject and item (i.e., picture); SyntaxType only varies by subject. When I dummy coded my variables using contr.treatment(), the model with the main effect of Listener removed from the fixed effects comes out identical to the full model: SoleTrain = read.table(paste(path, "SoleTrain.dat", sep=""), header=T) SoleTrain$Listener.f = factor(SoleTrain$Listener, labels=c("E1", "E2")) contrasts(SoleTrain$Listener.f) = contr.treatment(2) SoleTrain$SyntaxType.f = factor(SoleTrain$SyntaxType, labels=c("Transitive", "Locative", "Dative")) contrasts(SoleTrain$SyntaxType.f) = contr.treatment(3) # Create full model: SoleTrain.full<- glmer(Target_E2_pref ~ Listener.f*SyntaxType.f + (1 + Listener.f*SyntaxType.f|Subject) + (1 + Listener.f|Picture), data = SoleTrain, family = binomial, verbose=T, control=glmerControl(optCtrl=list(maxfun=20000))) # Create model with main effect of Listener removed: SoleTrain.noListener<- glmer(Target_E2_pref ~ SyntaxType.f + Listener.f:SyntaxType.f + (1 + Listener.f*SyntaxType.f|Subject) + (1 + Listener.f|Picture), data = SoleTrain, family = binomial, verbose=T, control=glmerControl(optCtrl=list(maxfun=20000))) > anova(SoleTrain.full, SoleTrain.noListener) Data: SoleTrain Models: SoleTrain.full: Target_E2_pref ~ Listener.f * SyntaxType.f + (1 + Listener.f * SyntaxType.f | Subject) + (1 + Listener.f | Picture) SoleTrain.noListener: Target_E2_pref ~ SyntaxType.f + Listener.f:SyntaxType.f + (1 + Listener.f * SyntaxType.f | Subject) + (1 + Listener.f | Picture) Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) SoleTrain.full 30 2732.5 2908.5 -1336.2 2672.5 SoleTrain.noListener 30 2732.5 2908.5 -1336.2 2672.5 0 0 1 However, I don't have this problem when I test for the main effect of SyntaxType, and remove the SyntaxType.f factor from the fixed effects. (That is, this produces a different model than the full model.) Someone suggested that Helmert coding was better for factors with more than two levels, so I tried running the same models except with Helmert coding [contrasts(SoleTrain$SyntaxType.f) = contr.helmert(3)], but the models come out identical to the way they do with dummy coding. So why does the model with the main effect of Listener removed the same as the model with the main effect of Listener retained? Any suggestions as to what I'm doing wrong? Thanks! Rachel