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From |
Yuval Arbel <yuval.arbel@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Mean test in a Likert Scale |

Date |
Thu, 6 Sep 2012 11:55:56 +0300 |

Maarteen, I didn't quite follow the statement you made regarding free lunch and restricted models. I thought the less restrictive the model is the more powerful it is. It is well known, for example, that estimation of unrestricted models always yield higher R-squares and higher log-likelihood - so the LR statistics (or F-Statistics) for testing the validity of a restriction imposed on a model have to be positive On Mon, Sep 3, 2012 at 11:21 PM, Cameron McIntosh <cnm100@hotmail.com> wrote: > This paper might also be of interest: > > Wu, C.-H. (2007). An empirical study on the transformation of likert-scale data to numerical scores. Applied Mathematical Sciences, 1(58), 2851-2862. > http://www.m-hikari.com/ams/ams-password-2007/ams-password57-60-2007/wuchienhoAMS57-60-2007.pdf > > Cam > >> Date: Mon, 3 Sep 2012 15:44:30 -0500 >> To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu >> From: richardwilliams.ndu@gmail.com >> Subject: Re: st: RE: Mean test in a Likert Scale >> >> At 11:00 AM 9/3/2012, Maarten Buis wrote: >> >On Mon, Sep 3, 2012 at 4:54 PM, Yuval Arbel wrote: >> > > Nick and Maarten, Note, that Kmenta's message is to prefer models with >> > > less restrictions. >> > >> >As always, there is no such thing as a free lunch. Less restrictions >> >typically cost statistical power, and if the restriction works well >> >for a particular applications, not using it will be a waste. Moreover, >> >such statements are in practice used to prefer models with less known >> >restrictions over models with well known restrictions. For example, I >> >have seen it used to prefer an -oprobit- over an -ologit- because >> >-ologit- implies the proportional odds assumption and -oprobit- >> >implies an equivalent assumption with a less memorable name. >> >> I had a fairly prominent econometrician make that argument to me >> once. My response was that both ologit and oprobit require what has >> been called the parallel lines or parallel regressions assumption to >> be met. It just so happens that, with ologit, if parallel lines holds >> then proportional odds will hold too. But it isn't like ologit has an >> additional hurdle to clear; it is just that if it clears the parallel >> lines hurdle, it simultaneously clears the proportional odds hurdle too. >> >> >> > > Moreover, are you suggesting we can deal in the same manner with >> > > quantitative values and ordinal variables? if our independent >> > > variables are what subjects marked on a questionnaire on a scale >> > > between 1 to 5 is the statistical treatment within a regression >> > > analysis framework should be identical to an independent variable >> > > measured in US dollars? >> > >> >No, all I am saying is that I do not rule out that there exists an >> >application where treating a ordinal variable as having a linear >> >effect works well enough and that it is worth checking whether that is >> >the case, as you can safe a lot of power that way. Moreover, an amount >> >in dollars may not be as cardinal as one might hope; often respondents >> >round their answers considerably even if asked to provide exact >> >answers. >> >> Maybe this has already been mentioned, but pages 421-422 of Long & >> Freese (2006) show how to test whether an ordinal independent >> variable can be treated as though it were interval. See >> >> http://www.stata.com/bookstore/regression-models-categorical-dependent-variables/index.html >> >> ------------------------------------------- >> Richard Williams, Notre Dame Dept of Sociology >> OFFICE: (574)631-6668, (574)631-6463 >> HOME: (574)289-5227 >> EMAIL: Richard.A.Williams.5@ND.Edu >> WWW: http://www.nd.edu/~rwilliam >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ -- Dr. Yuval Arbel School of Business Carmel Academic Center 4 Shaar Palmer Street, Haifa 33031, Israel e-mail1: yuval.arbel@carmel.ac.il e-mail2: yuval.arbel@gmail.com * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: Mean test in a Likert Scale***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: RE: Mean test in a Likert Scale***From:*Yuval Arbel <yuval.arbel@gmail.com>

**References**:**Re: st: RE: Mean test in a Likert Scale***From:*Ulrich Kohler <kohler@wzb.eu>

**Re: st: RE: Mean test in a Likert Scale***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: RE: Mean test in a Likert Scale***From:*Ulrich Kohler <kohler@wzb.eu>

**Re: st: RE: Mean test in a Likert Scale***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: RE: Mean test in a Likert Scale***From:*Yuval Arbel <yuval.arbel@gmail.com>

**Re: st: RE: Mean test in a Likert Scale***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: RE: Mean test in a Likert Scale***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: RE: Mean test in a Likert Scale***From:*Yuval Arbel <yuval.arbel@gmail.com>

**Re: st: RE: Mean test in a Likert Scale***From:*Richard Williams <richardwilliams.ndu@gmail.com>

**RE: st: RE: Mean test in a Likert Scale***From:*Cameron McIntosh <cnm100@hotmail.com>

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