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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Chi-square test for Categorical Data Analysis |

Date |
Wed, 19 Sep 2007 19:17:45 +0100 |

Orthogonal to this, but pertinent, is to note that Harold Jeffreys' "Theory of probability" (which might have been called "Bayesian statistics and data analysis", except that that title was unthinkable in 1939, when it was first published) has a discussion of "wishful thinking" as one of the pitfalls in statistical work. Arguably every statistical text needs a strong reminder that "wishful thinking" is a source of many pitfalls. Nick n.j.cox@durham.ac.uk Austin Nichols > David Radwin <radwin@berkeley.edu>: > Any technique may lead you astray, but the technique you describe is, > I suspect, demonstrably inferior to others. The reference you cite > applies only to estimating the mean or median of an open-ended > category with a Pareto distribution, and its abstract says that "the > choice of method and the selection of a mean or median estimator for > the open-ended category midpoint have substantial effects on analyses > in which income is the dependent variable." > > The -intreg- approach proposed by Maarten relies on some shaky > assumptions, but I expect it is far preferable to imputing the > midpoint of intervals and testing for a difference in means as if you > have real data. > > On 9/19/07, David Radwin <radwin@berkeley.edu> wrote: > > It is true, of course, as with many statistical techniques, > that this > > technique may lead you astray. I have not done any simulations > > myself, but I will refer you again to the reference in my original > > posting: > > > > Parker, R. N., & Fenwick, R. (1983). The Pareto curve and > its utility > > for open-ended income distributions in survey research. Social > > Forces, Vol. 61, No. 3, 872-885. > > http://www.jstor.org/view/00377732/di010900/01p0014t/0 > > > > David > > > > At 11:31 AM -0400 9/19/07, Austin Nichols wrote: > > >David Radwin <radwin@berkeley.edu>: > > > > > >I think it's fairly easy to prove via counterexample or simulation > > >that this can easily give the wrong answer. Can you give > a reference > > >that supports it? * * 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/

**References**:**Re: st: Chi-square test for Categorical Data Analysis***From:*"Austin Nichols" <austinnichols@gmail.com>

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