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RE: st: RE: Quantile question


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: Quantile question
Date   Sun, 2 Mar 2008 17:01:15 -0000

My comment was purely about grouping. Information is lost by grouping;
that's my 
only point. 

I  don't understand what you understand by quantile here. In particular,

what is a "1-quantile" increase? Perhaps you mean something like
percentile rank, 
in effect the inverse of the quantile function. 

Nick 
n.j.cox@durham.ac.uk 

Ronan Conroy

On 29 Feb 2008, at 17:59, Nick Cox wrote:

> It's not your question, but most classifications into quantile-based
> groups sound perverse.
> Why throw information away?

Psychometrics

As predictor variables, quantiles have natural advantages over scores  
on psychometric instruments such as depression scales or aptitude  
scales. These advantages are

1. Easy to understand: the effect size is the effect of a 1-quantile  
increase in the predictor
2. Easy to compare: effect sizes for different predictors are  
comparable, allowing comparisons to be made
3. The quantiles are based on the actual distribution of the measure  
in the population, not on its theoretical score range. The adjust for  
the often far-from-nice distributions shown by psychometric measures.

In fact, where the predictor is measured on an unfamiliar and  
arbitrary scale, quantiles contain more information (in the  
hermeneutic rather than Shannonist sense) than the original scale  
values.


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