Hello,

you might like this one:

Murtaugh, Paul A. 2007. SIMPLICITY AND COMPLEXITY IN ECOLOGICAL DATA 
ANALYSIS. Ecology 88:56--62. 
[doi:10.1890/0012-9658(2007)88[56:SACIED]2.0.CO;2]
http://www.esajournals.org/doi/abs/10.1890/0012-9658(2007)88[56:SACIED]2.0.CO;2 
<http://www.esajournals.org/doi/abs/10.1890/0012-9658%282007%2988[56:SACIED]2.0.CO;2>

Abstract
I argue that ecological data analyses are often needlessly complicated, 
and I present two examples of published analyses for which simpler 
alternatives are available. Unnecessary complexity is often introduced 
when analysts focus on subunits of the key experimental or observational 
units in a study, or use a very general framework to present an analysis 
that is a simple special case. Simpler analyses are easier to explain 
and understand; they clarify what the key units in a study are; they 
reduce the chances for computational mistakes; and they are more likely 
to lead to the same conclusions when applied by different analysts to 
the same data.


Cheers,

Luca




Le 23/02/2011 00:45, Derek Sonderegger a écrit :
> I am trying to fully understand the trade-offs between the two analyses and
> if one method is superior in the case of only two observations per random
> effect.
>
> Full experimental set-up: I have 6 field plots and three of them receive a
> treatment (applied to the plot) and three serve as controls.  In each plot,
> I have measurements from 2 individual plants.  Since this is a desert
> ecosystem, the variance within a plot is quite large compared the the
> variance from plot to plot.
>
> The 'old-school' analysis method would be to calculate plot average for each
> plot and do an ANOVA using those plot means as data and accept that my
> inference is only at the plot level.  If I go with a fancy mixed model
> approach, my inference is on the level of an individual plant, and I get an
> estimate of within plot variability.
>
> But the rub is this:  the plot means approach gives me a fairly significant
> treatment effect (p = .014) while the mixed model approach gives me a
> non-significant result (p=.34).  My feeling is that trying to estimate the
> random effect (in addition to the treatment effect) is taking a large amount
> of our statistical power in a relatively data poor problem.  I did a quick
> simulation study and it appears that in the case of high within plot
> variability and a small number of observations per plot, the mixed model
> approach has substantially reduced power compared to the plot means
>
> > From a scientific point of view, we actually don't mind making inferences at
> the plot level as we are primarily concerned with landscape effects of the
> treatment.
>
> At this point I think that the plot means approach is the best choice, but I
> want to make sure that I'm not missing anything.
>
> Derek Sonderegger
>
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