[R-sig-ME] sample size in glmer model

Thierry Onkelinx th|erry@onke||nx @end|ng |rom |nbo@be
Thu Jan 23 11:39:05 CET 2020

Dear Souheyla,

Your fixed effects need 4 parameters: intercept, V1, TimeLevel2 and
(Time|Participant) needs 3 parameters: variance of TimeLevel1, variance of
TimeLevel2 and their covariance.
The same goes for (Time|Item).
So you're using 10 parameters.

(Time|Participant) tries to estimate the difference between two times for
each participant. Such random effect requires much more information than
(1|Participant). In this case I'd recommended that you have 10 or more
observations for the majority of the Time/Participant combinations.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel

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than asking him to perform a post-mortem examination: he may be able to say
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.
~ John Tukey


Op do 23 jan. 2020 om 11:15 schreef Souheyla GHEBGHOUB <
souheyla.ghebghoub using gmail.com>:

> Morning Ben,
> I am not doing (item|Participant).
> V1 is a duration (continuous) , I should have an interaction of Time of two
> levels  :  score ~ V1*Time + (Time|Participant) + (Time|item) ,
> This means I have 7 parameters including intercept (4 random effects +
> intercept + V1 + TimeLevel1).
> I have 1484 observations and 53 participants.  The rule is 10 per
> parameter.
> My question: Is it 10 participants per parameter (i.e. I am allowed 5
> parameters) or 10 observation per parameter (I am allowed 148
> parameters!!!)
> I guess it is likely per participant, and if its the case, then should I
> still report results even the power is less (53 divided by 7 = I have 7.5
> participants per parameter, not 10 as recommended)
> Thats all
> Thank you,
> Souheyla
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