[R-sig-ME] Testing assumption multilevel analysis

Phillip Alday ph||||p@@|d@y @end|ng |rom mp|@n|
Mon Aug 26 17:35:00 CEST 2019

Please keep the list in CC.

As Ben Bolker mentioned in his reply: for most things, the assumptions
carry over from the non-mixed case and the graphical diagnostics are
done the same way. I would in general avoid explicit statistical tests
of model assumptions (e.g. various tests of normality) because, like all
tests, they have failure modes (especially related to sensitivity and
specificity) and don't actually tell you what any potential violation of
assumptions is doing to your statistical procedure.

For multicollinearity, there is one additional diagnostic that lme4
gives you in its summary output, namely the correlation of fixed
effects. The exact meaning of this is perhaps a little technical
but in practical terms a high correlation suggests that there may be
multicollinearity. Multicollinearity also tends to show itself in
inflated standard errors (in the fixed effects), much as it does for
standard linear regression.

Regarding independence of errors: I find that to be an assumption that
is often best checked by knowing something about your data generating
process. For example, there may be some autocorrelation in the errors
between observations due to the way data are collected.


On 26/8/19 2:23 pm, Katharina Tostmann wrote:
> Hello Phillip,
> Yes, I know it is a very big question about the assumptions in general.
> At this time I got a little information about linearity, normal
> distibution and variance homogenity. But what ist about
> mulitcollinearity and independency? Do you have any idea to check this
> in a multilevel context?
> Thank you in advance.
> best regards from Germany
> Katharina
> Am Mo., 26. Aug. 2019 um 14:14 Uhr schrieb Phillip Alday
> <phillip.alday using mpi.nl <mailto:phillip.alday using mpi.nl>>:
>     This is a rather open-ended request -- you're more likely to get helpful
>     advice if you're a bit more specific. For example, which model
>     assumptions do you want to test in particular? What do your data look
>     like? Which assumptions do you think your data might violate? Why do you
>     want to explicitly test assumptions? (e.g. Are you worried about
>     inflated Type-I error? Often it's better to worry less about assumptions
>     per se and instead focus on "does my model capture the relevant aspects
>     of my data?")
>     Phillip
>     On 24/8/19 11:08 am, Katharina Tostmann wrote:
>     > Hello together,
>     >
>     > I'm calculating a multi-level analysis in R. However, I do not
>     understand
>     > how to test the model assumptions. In my second hypothesis I also
>     have a
>     > mediation with, whereby I also have no idea how to test the model
>     > assumptions.
>     > Can anyone help here? Thank you and best regards
>     >
>     > Katharina
>     >
>     >       [[alternative HTML version deleted]]
>     >
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