Dear all,

I am aiming to assess the inter-individual variability of both random
intercept and slope in response to multiple changing variables.
In order to so, several studies have compared models two by two by changing
their structure. For example, to know whether there is a difference in the
plasticity of the responses between individuals, they compare a model with
both the interest variable*Identity individual as random effect and a model
with only "Identity individual" ad random effect. They then realize a
loglikelihood test and base their results only on the pvalues.

 I am looking for an alternative as I have been strongly recommended to
base my results on effect size (and 95% IC) rather than on pvalues. This
has indeed several advantages as it gives the biological magnitude of an
effect, its uncertainty and it is comparable between studies.

 It is here where I am asking for some help!
 I thought that I could use a similar approach (explained above) by basing
my results on the AIC rather than the pvalue. The thing is it is not
working when I compare a model with a random effect and a model without...
What to do then?
 I have also heard about parametric bootstrapping but I am quite unsure how
to use this method.

 This is really important to me and some help would be very appreciated!

 Thank you!


-- 
*Tommy Gaillard
**Intern student CNRS*
Burgundy University (France)
Master 2 Research
Tel: 06-71-81-94-66
Email: tommy.gaillard40@gmail.com

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