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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. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Nonparametric Methods for Longitudinal Data***From:*Thomas Herold <thomasherold@gmx.net>

**Re: st: Nonparametric Methods for Longitudinal Data***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Nonparametric Methods for Longitudinal Data***From:*Thomas Herold <thomasherold@gmx.net>

**Re: st: Nonparametric Methods for Longitudinal Data***From:*Nick Cox <njcoxstata@gmail.com>

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