sample size in glmer model

Dear Souheyla,

Your fixed effects need 4 parameters: intercept, V1, TimeLevel2 and
V1:TimeLevel2
(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
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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Op do 23 jan. 2020 om 11:15 schreef Souheyla GHEBGHOUB <
souheyla.ghebghoub at 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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