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RE: st: Looking for courses in non-linear modelling and imputation techniques


From   "Jacobs, David" <jacobs.184@sociology.osu.edu>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Looking for courses in non-linear modelling and imputation techniques
Date   Mon, 10 Oct 2011 18:46:49 +0000

Splines seem extremely useful, but referees (in sociology at least) often give me trouble about the theoretical justification for particular cut points when I use this specification.  And sociological theory isn't terribly informative about such issues.

I certainly agree that quadratics or for that matter cubics (which soc. referees also don't like) are procrustean, while dummies throw away information.  

Does anyone on the list have advice about how to justify splines when theory in one's discipline (and others) is mute about cut points?

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten Buis
Sent: Monday, October 10, 2011 12:56 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Looking for courses in non-linear modelling and imputation techniques

On Mon, Oct 10, 2011 at 6:02 PM, Sofia Ramiro wrote:
> I want to explore non-linear relationships between outcomes that have so far
> been analyzed as if their relationship was linear. For this, I need to learn
> some statistical techniques to explore these relationships (besides normal
> regression, or even generalized estimation equations), if I am not wrong.
> This is more difficult to find in normal courses, at least the ones I have
> been finding, as they focus on linear relationships between variables...

This is actually routinely discussed in introductory regression
courses. The standard remedies depends on the discipline: either one
splits the linear variable up in categories and adds dummies/indicator
variables for those categories or one adds a square term. These
standard remedies tend to either trow away too much information
(dummies) or impose too much structure and thus not fit very well
(square term). Personally, I like adding linear splines (see: -help
mkspline-) as a nice compromise between adding a non-linear effect and
interpretable coefficients. Another option is fractional polynomials
(see: -help fracpoly- and Royston and Sauerbrei 2008).

Hope this helps,
Maarten

Royston, P., and W. Sauerbrei. 2008. Multivariable Model-building: A
Pragmatic Approach to Regression Analysis
Based on Fractional Polynomials for Modelling Continuous Variables.
Chichester, UK: Wiley.

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany


http://www.maartenbuis.nl
--------------------------
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