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Re: st: Nonparametric Methods for Longitudinal Data

From   Nick Cox <>
Subject   Re: st: Nonparametric Methods for Longitudinal Data
Date   Mon, 11 Feb 2013 12:34:00 +0000

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.

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.

"Stata" is so spelled.


On Mon, Feb 11, 2013 at 12:16 PM, Thomas Herold <> wrote:

> I am currently analysing a dataset on the influence of certain treatments on
> depression. We have three different treatment groups and five points of
> measurement.
> The problem is that it has recently been discovered that the depression
> score we are working with can only be interpreted as ordinal data. What´s
> more, the resulting variable is far from being normally distributed - the
> data is just not suitable for parametric analysis.
> Concerning the independent variables: Some of them change over time (e.g.
> financial situation), others are time-invariant (e.g. treatment).
> Is there any nonparametric model for longitudinal data in STATA? Does anyone
> have any reading tips?

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