Post-hoc analysis of logistic mixed effects model

Dear list,
I am analyzing data with a logistic mixed effects model.
I created the following model.

mod1<-glmer(binomial_resposne ~ FactorA*FactorB*FactorC 
+(1+FactorA|subject)+(1+FactorA|speaker/item),family=binomial,data=mydata,control=glmerControl(optimizer="bobyqa", 
optCtrl=list(maxfun=1000)))

FactorA is a categorical variable with 2 levels, "A1" and "A2".
FactorB is a categorical variable with 3 levels, "B1", "B2", and "B3".
FactorC is a continuous variable.

To analyze the main effect of each factor and their interactions, I 
used Anova() with the car package, and reported the results of main 
effects of each factor in a paper (e.g., ?2(2) = 20.11, p < 0.001).
To do post-hoc analyses, I made orthogonal contrasts for each factor, 
and used the summary() function. I reported the b-value, SE, z-value 
and p-value for the significant effects (e.g.,? = 0.04, SE = 0.01, z = 
2.76, p < 0.01).

Is it possible to stick to one method of the analyses, rather than 
using two different methods (chi-square and regression coefficients)?
So my questions are as follows.
1. If I stick to the regression coefficient method with the summary() 
function, how could I get the results of the main analysis for FactorB 
with 3 levels?
2. If I stick to the chi-square method with the Anova() function, how 
could I do post-hoc tests with the same method (not lsmeans or glht)?

Thank you very much for your help in advance.

Best wishes,
Yasu