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Re: st: Nonparametric Methods for Longitudinal Data


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Nonparametric Methods for Longitudinal Data
Date   Mon, 11 Feb 2013 13:58:00 +0000

I should add that I don't know about HADS and have not attempted to
read about it.


On Mon, Feb 11, 2013 at 1:55 PM, Nick Cox <njcoxstata@gmail.com> wrote:
> This is contentious territory. I give preference to my own views, but
> I think they are widely shared.
>
> There is still widespread reference to "non-parametric statistics",
> but its complement "parametric statistics" is not a term I think now
> used by many statistically-minded people who are reasonably
> well-informed and up-to-date, not least because there is little in
> common to the rest of statistics, except that of not being
> "non-parametric". It is my strong impression that  "parametric
> statistics" is a kind of back-formation of terminology from some
> social scientist textbook writers in the 1950s or 1960s who wanted a
> name to contrast with the methods they were advocating. It predates
> most of the methods for categorical data now usually disussed under
> that heading.
>
> Quibbles aside, I just would not agree that using such methods to
> ordinal data is "simply wrong". That's absolute and definite when
> there are many, many exceptions. For example, I am very happy to apply
> Pearson correlation to ranked data when it seems appropriate: that's
> Spearman correlation in another guise. I am even happy to average
> grades that are assigned on an ordinal scale; indeed some fraction of
> my day job is based on that practice.
>
> See also references given in
> http://www.stata.com/statalist/archive/2012-08/msg01401.html
>
> You have a bigger problem yet, in that it's my impression that
> non-parametric methods for longitudinal data is a largely empty
> category. The framework of non-parametric methods does not really
> extend without breaking to multiple predictors and panel structure,
> let alone any time dependence too. It would be good if Roger Newson's
> programs solved your problem, but I fear they won't.
>
> If this were my problem, I think I would be looking at -xt- commands,
> just treading especially carefully.
>
> Nick
>
> On Mon, Feb 11, 2013 at 1:32 PM, Thomas Herold <thomasherold@gmx.net> wrote:
>> Dear Nick,
>>
>> Thank you very much for your answer.
>>
>>> Questions like this raise more questions in their wake.
>>>
>>> It is a bit puzzling that you have apparently only just discovered how
>>> your response variable is defined. However, many medical and psychiatric
>>> analyses make use of scores usually devised according to the answers to
>>> multiple questions. They often work at least
>>> approximately like measured variables; many researchers would argue that
>>> treating them as ordinal is too pessimistic and indeed there are
>>> usually too many distinct values for many standard models for ordinal
>>> responses to work well.
>>>
>>> IQ is an example familar to many.
>>
>> I have not been involved in the design of the study. I was just asked to
>> evaluate it. You certainly have a point when you say that my approach might
>> be too pessimistic. However, it seems to be common belief that the scale I
>> am talking about (Hospital Anxiety and Depression Scale - HADS) only
>> generates ordinal data. And if we take this seriously we have to admit that
>> one basic assumption of parametric analysis is not fulfilled. Would you
>> agree with that?
>>
>>
>>>
>>> Statistically, it's a myth on several levels that "parametric analysis"
>>> requires a response variable to be normally distributed. At
>>> most, it's a secondary assumption of some regression-like methods that
>>> error disturbances be normally distributed. There are also many
>>> methods that are not non-parametric for other distributions (exponential,
>>> gamma, etc., etc.).  Also, what about transformations or
>>> similar link functions.
>>> So, manifestly I can't see your data but I'd suggest that your impression
>>> that you need quite different methods is jumping to conclusions prematurely.
>>>
>> Again, I could not agree more. However, that does not really answer my
>> question. You have to know that my statistical background knowledge is
>> limited. For example, I would have thought that using parametric methods for
>> ordinal data is not only "optimistic" but simply wrong.
>> Therefore, I would highly appreciate it if you (or someone else) could tell
>> me a regression type that is supported by Stata (sorry for the spelling
>> error in my first post) and might be worth having a look at in this context.
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