# [R-sig-ME] Using variance components of lmer for ICC computation in reliability study

Doran, Harold HDor@n @ending from @ir@org
Thu Jun 14 19:35:11 CEST 2018

```Well no, you¹re specification is not right because your variable is not
continuous as you note. Continuous means it is a real number between
-Inf/Inf and you have boundaries between 1 and 10. So, you should not be
using a linear model assuming the outcome is continuous.

On 6/14/18, 11:16 AM, "Bernard Liew" <B.Liew using bham.ac.uk> wrote:

>Dear Community,
>
>
>I am doing a reliability study, using the methods of
>https://www.ncbi.nlm.nih.gov/pubmed/28505546. I have a question on the
>lmer formulation and the use of the variance components.
>
>Background: I have 20 subjects, 2 fixed raters, 2 testing sessions, and
>10 trials per sessions. my dependent variable is a continuous variable
>(scale 1-10). Sessions are nested within each subject-assessor
>combination. I desire a ICC (3) formulation of inter-rater and
>inter-session reliability from the variance components.
>
>My lmer model is:
>
>lmer (dv ~ rater + (1|subj) + (1|subj:session), data = df)
>
>Question:
>
>  1.  is the model formulation right? and is my interpretation of the
>variance components for ICC below right?
>  2.  inter-rater ICC = var (subj) / (var(subj) + var (residual)) # I
>read that the variation of raters will be lumped with the residual
>  3.  inter-session ICC =( var (subj) + var (residual)) /( var (subj) +
>var (subj:session) + var (residual))
>some simulated data:
>df = expand.grid(subj = c(1:20), rater = c(1:2), session = c(1:2), trial
>= c(1:10))
>df\$vas = rnorm (nrow (df_sim), mean = 3, sd = 1.5)
>
>I appreciate the kind response.
>
>Kind regards,
>Bernard
>
>
>
>	[[alternative HTML version deleted]]
>
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>

```