[R-sig-ME] sample size in glmer model
Ben Bolker
bbo|ker @end|ng |rom gm@||@com
Wed Jan 22 22:13:25 CET 2020
[please keep r-sig-mixed-models in the Cc: list when replying]
What is V1, what level does it vary at (among vs within
participants), and what are you interested in comparing/testing with
this model?
Note that Barr et al 2013's "keep it maximal" protocol would
suggest that you fit (item|Participant) (but ... without
factor-analytic models or some form of regularization this will never
actually be practical, as you'd be trying to estimate a 28x28
covariance matrix (= 378 parameters) from 53 participants ... you
usually have to choose from some more restricted set of choices
[independence, compound symmetry, etc.]
On Wed, Jan 22, 2020 at 12:36 PM Souheyla GHEBGHOUB
<souheyla.ghebghoub using gmail.com> wrote:
>
> Hi Ben,
>
> I am not comparing between participants, it is a within group design, each participant answered 28 items
> My model is glmer ( score~ V1 + (1|Participant) + (1|item) ??
>
> Thank you,
> Sou
>
> On Wed, 22 Jan 2020 at 16:21, Ben Bolker <bbolker using gmail.com> wrote:
>>
>> It's going to depend on whether the parameters you're trying to
>> estimate are capturing processes that vary at the level of
>> participants or at the level of observations. For example, if you
>> wanted to compare trends over time among groups of participants, each
>> measured multiple times (i.e. a random-slopes model with a
>> fixed-effect interaction between treatment and slope) you'd need to
>> consider the number of participants. (In a sense the answer to this
>> question goes back to the classical experimental design bestiaries of
>> nested vs randomized block vs split-plot vs ... and deciding what the
>> "denominator degrees of freedom" are supposed to be in each case/for
>> each test of interest).
>>
>> On Wed, Jan 22, 2020 at 9:45 AM Thierry Onkelinx via
>> R-sig-mixed-models <r-sig-mixed-models using r-project.org> wrote:
>> >
>> > Dear Sou,
>> >
>> > I'd suggest at least 10-15 observation per **parameter**. Categorical
>> > variables with more than two levels require more than one parameter. An
>> > interaction between two continuous variable requires 3 parameters (2 main
>> > effect + 1 interaction). Don't forget to count the hyperparameters of the
>> > random effects.
>> >
>> > Best regards,
>> >
>> > Thierry
>> >
>> > ir. Thierry Onkelinx
>> > Statisticus / Statistician
>> >
>> > Vlaamse Overheid / Government of Flanders
>> > INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
>> > FOREST
>> > Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
>> > thierry.onkelinx using inbo.be
>> > Havenlaan 88 bus 73, 1000 Brussel
>> > www.inbo.be
>> >
>> > ///////////////////////////////////////////////////////////////////////////////////////////
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>> > what the experiment died of. ~ Sir Ronald Aylmer Fisher
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>> > ensure that a reasonable answer can be extracted from a given body of data.
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>> >
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>> >
>> >
>> > Op wo 22 jan. 2020 om 14:55 schreef Souheyla GHEBGHOUB <
>> > souheyla.ghebghoub using gmail.com>:
>> >
>> > > Hi,
>> > > In Field (2012) we need 10-15 *participants* per variable for regression,
>> > > in Levshina (2015) we need 10-15 *observations* per variable.
>> > >
>> > > Is it participant or observation? I am so confused as I have 53
>> > > participants and 1484 observations.
>> > >
>> > > Thank you,
>> > > Sou
>> > > PhD in Education
>> > > University of York
>> > >
>> > > [[alternative HTML version deleted]]
>> > >
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