[R-sig-ME] Using mixed-effects modelling to estimate between- and within-Ss variance in an effect

[Douglas Bates]

A quick question of clarification, does the notation Ss stand for
"subject-stimulus"?

Perhaps you could follow up with a few sentences giving a bit more
background on the type of experiments that you have in mind.

On Wed, Jul 7, 2010 at 10:57 AM, Mike Lawrence <Mike.Lawrence at dal.ca> wrote:
> Hi folks,
>
> In psychology, we're often interested not only in effects, but also
> their variability. This is mostly from a pragmatic perspective, where
> we want to know how much time to devote to measuring a certain
> phenomenon in order to reliably obtain an expected effect.
> Historically, variability has been quantified with a single measure of
> "reliability" (typically obtained by correlating subsets of
> measurements). More recently, it has been suggested that such single
> measures confound two sources of variability that are of potentially
> independent interest: between-Ss variability of the effect, and
> within-Ss variability of the effect. That is, we typically compute
> effects based on many observations per Ss, so variability of the
> effect is theoretically computable within each Ss.
>
> Prior to my exposure to mixed-effects modelling, I used bootstrap
> resampling to estimate the between- and within-Ss variabilities of the
> effect, but now that I have dipped my toes into mixed-effects
> modelling, I suspect that these values might be already estimated
> automatically as part of the mixed-effects modelling procedures. Is
> this the case, and if so, how could I obtain these estimates from,
> say, the output from lmer?
>
> Mike
>
> --
> Mike Lawrence
> Graduate Student
> Department of Psychology
> Dalhousie University
>
> Looking to arrange a meeting? Check my public calendar:
> http://tr.im/mikes_public_calendar
>
> ~ Certainty is folly... I think. ~
>
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