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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. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: Quantile question***From:*Ronan Conroy <rconroy@rcsi.ie>

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