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Re: st: RE: Mean test in a Likert Scale
From
Yuval Arbel <[email protected]>
To
[email protected]
Subject
Re: st: RE: Mean test in a Likert Scale
Date
Thu, 6 Sep 2012 12:06:52 +0300
Maarten, am I correct by saying you imply a second type error, namely
a rejection of the restriction in the case you should not have
rejected it?
On Thu, Sep 6, 2012 at 11:55 AM, Yuval Arbel <[email protected]> wrote:
> 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 <[email protected]> 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: [email protected]; [email protected]
>>> From: [email protected]
>>> 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: [email protected]
>>> WWW: http://www.nd.edu/~rwilliam
>>>
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>>
>> *
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>
>
>
> --
> Dr. Yuval Arbel
> School of Business
> Carmel Academic Center
> 4 Shaar Palmer Street,
> Haifa 33031, Israel
> e-mail1: [email protected]
> e-mail2: [email protected]
--
Dr. Yuval Arbel
School of Business
Carmel Academic Center
4 Shaar Palmer Street,
Haifa 33031, Israel
e-mail1: [email protected]
e-mail2: [email protected]
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