Thank you very much for your comments.

>> 
>> 
>> 
>> 1. Is the procedure at arriving at a minimal adequate model the same
>> as for logistic regression models (glm)? 
> 
>  More or less, although there is some debate as to whether one should
> try to discard non-significant random-effects terms or not: see Barr et
> al 2013 (ref below), and whether one should consider fixed or random
> effects first (I am personally uneasy with the concept of "minimal
> adequate models" for confirmatory testing in the first place).

Actually I do not want to discard random effects. I just want to delete a fixed effects predictor (Age of speaker) which has an insignificant p-value, both in the mixed effects-model as well as in the generalized linear model (glm). All other predictors are significant. Only a number of interactions are also insignificant. I would also delete those.
> 
> 2. How do I find out whether
>> there is reason to assume overdispersion? 
> 
>  See http://glmm.wikidot.com/faq#overdispersion_est and following (note
> that overdispersion is unidentifiable in the case of binary data with
> unique predictors, and already taken into account in models with
> estimated scale parameters [Gaussian, gamma, etc.])

This makes sense!
> 
> 3. How do I find out
>> whether my models (both the initial and the final model) have
>> predictive power? 
> 
>  Don't know exactly what you mean here.  You could look at
> http://glmm.wikidot.com/faq#rsquared

I was thinking about the C-index, which serves as a useful measure in GLMs. I have seen that it is used in some studies using GLMMs but I have also encountered studies that argue against the usefulness of the C-index in GLMMs. I have problems computing it though.
> 
> 4. How do I determine whether interspeaker
>> variability (File) is stronger than the fixed effects?
> 
>  As a first cut, comparing the magnitude of the standard deviation
> estimate to the size of the fixed effects should do (assuming that the
> fixed effect predictors are appropriately scaled).  Beyond that, it
> would depend exactly what you mean.

Actually I want to show that one group of my speakers shows more variability in the use of future time markers than the other. The groups serve as fixed effects predictors, the individual conversations as random effects. I want to compare the results to a GLM including the same predictors, but without the random effects, and show that only when including random effects the overall variation can be explained.
> 
>> 
>> I am grateful for any comment on my questions!! Thanks Agnes
>> 
>> _______________________________________________ 
>> R-sig-mixed-models@r-project.org mailing list 
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>> 
> 
> 
> @article{barr_random_2013,
> 	title = {Random effects structure for confirmatory hypothesis testing:
> Keep it maximal},
> 	volume = {68},
> 	issn = {{0749596X}},
> 	shorttitle = {Random effects structure for confirmatory hypothesis
> testing},
> 	url = {http://linkinghub.elsevier.com/retrieve/pii/S0749596X12001180},
> 	doi = {10.1016/j.jml.2012.11.001},
> 	number = {3},
> 	urldate = {2013-06-04},
> 	journal = {Journal of Memory and Language},
> 	author = {Barr, Dale J. and Levy, Roger and Scheepers, Christoph and
> Tily, Harry J.},
> 	month = apr,
> 	year = {2013},
> 	pages = {255--278},
> }


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