[R-sig-ME] Most principled reporting of mixed-effect model regression coefficients

[João Veríssimo]

This one came out recently, may help (particularly Table 7):
https://doi.org/10.1016/j.jml.2020.104092

João

On Fri, 2020-02-14 at 07:59 +0000, Ades, James wrote:
> Hi all,
> 
> 
> 
> Its been surprisingly difficult to find the most principled reporting
> of mixed-effect model regression coefficients (for individual fixed-
> effects). One stack overflow article lead me to this papera
> systematic review of the incorporating and reporting of GLMMs ( 
> https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112653#pone.0112653.s001)
>   which references a paper by Ben Bolker (
> https://www.sciencedirect.com/science/article/pii/S0169534709000196).
> Oddly, I dont really find an answer to this in either of those. Ive
> heard mixed things regarding fixed effect coefficients in LMM (that
> LMM/and GLMMs are more about the predictive power of an entire model
> than the individual predictors themselves), but overall, my
> understanding is that its kosher (and informative) to look at effect
> sizes of regression (fixed effect) coefficientsonly that lme4 doesnt
> currently provide p values (though Lmertest does).
> 
> 
> 
> It seems like reporting effect size of regression coefficients and
> their SEs should suffice; though sometimes people report CI with
> those as well (but isnt that a little redundant). My PI is telling me
> to include p-values. So many different things, so little agreement.
> 
> 
> 
> I figured Id turn here for something of a definitive answer.
> 
> 
> 
> Ben, I definitely need to go back and read through your paper more
> thoroughly for a deeper understanding of the nuances of GLMMs.
> Currently watchingand readingMcElreaths Statistical Rethinking, but
> Im not quite at the level of implementing MCMCs.
> 
> 
> Much thanks,
> 
> 
> James
> 
> 
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> 
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