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On Wed, Jan 25, 2012 at 9:22 PM, William Hauser <email@example.com> wrote:
> Excellent point regarding reviewers. Regarding your point 1, I found
> it very strange that the model would not converge with the crime
> seriousness variable in it because, as you say, normality of the
> predictors isn't supposed to matter. But it does, or at least it
> seems to. I can't provide you with a mathematical proof but I can say
> that the model won't converge with either or both of the
> non-transformed predictors in the model (even as the sole predictor).
> At first I thought it was a property of the hierarchical model but the
> non-convergence also occurs in the ordinary non-nested logit model
> ("logit prison_sentence crime_seriousness, or"). The problem
> magically goes away when the variable is transformed.
> I thought about asking about this because, well, it runs contrary to
> everything I've been told but then I decided the issue was tangential
> to the bigger problem of how to transform and interpret the variables
> afterwards. It's a interesting question and one I'd love to hear
> speculation about but I can just side step the whole issue by
> transforming the variable.
> In case you're interested, both variables prevent convergence either
> singly or together. Prior record points has a skew of 1.8 and
> kurtosis of 7, crime seriousness points is more seriously non-normal
> and has a skew of 10.5 and a kurtosis of 520 (again, these are
> "points" assigned under a arbitrary scoring system). It looks to me
> like it's the overdispersion and not the skew that's creating problems
> but I have not a clue why.
> Will Hauser
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