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Re: st: Measures of fit in clogit

From   "Clive Nicholas" <[email protected]>
To   [email protected]
Subject   Re: st: Measures of fit in clogit
Date   Sun, 30 Jan 2005 23:17:06 -0000 (GMT)

Nakao Taniguchi wrote:

> I'd like to compare the fit of 2 models which predict vote choice in
> election.
> Supposed that samples (voter) have 5 alternatives (candidates), and they
> feel
> different utility for an each alternative.
> Models have 4 variables ( age, gender, education, adn utility), but
> utility is
> calculated in a different way across 2 models.  Samples are same.


> Questions:
> // Which model of them does a good prediction?

Assuming that 'utilities' means differing propensities to vote for
candidate/party X (almost certainly on a Likert scale), the fit statistics
generated by -fitstat- are almost identical. For instance, the McFadden
R^2 in Model A = .297; in Model B = .292. Therefore, I would conclude that
changing your voting propensity scale from one to the other makes very
little difference to your model's overall prediction of vote choice. (And
since you're using -clogit-, then presumably your model is seeking to
predict the _change_ or _stability_ in candidate choice from one election
to the next for the ith voter.)

> // How do I get the adjusted count Rsquare in clogit?

I'm not entirely clear by what you mean here, but if you simply want an
adjusted R^2, and you're happy with McFadden's R^2, then the adjusted
McFadden R^2 displayed to the right of the 'pure' version ought to be good
enough for you.

> // How do I get a cross table of the observed X the predicted values ?
> ('lstat' has already been installed, but does not work well...)

I'm afraid I've not used -clogit- in Stata enough to answer this, as I
couldn't find anything in -whelp clogit- to answer this properly. All I
can suggest is to -predict- and compare with the observed values. Not as
nice as having it all in a contingency table, but better than nothing!

> // What is the best measures of fit in clogit?

This has to be your call, but it is worth pointing out that virtually
every fit statistic across your two models broadcasts the same message:
changing utility scale makes little difference to your model fit. It is
interesting that you don't mention whether or not changing utility has any
impact upon the sign and/or magnitude of the _other_ three variables. If
they have little or no impact, this indicates that your model is robust to
such changes.

I hope all this helps.

CLIVE NICHOLAS        |t: 0(044)7903 397793
Politics              |e: [email protected]
Newcastle University  |

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