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Re: st: fit index for ordered logistic regression


From   Lucas <lucaselastic@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: fit index for ordered logistic regression
Date   Sat, 14 Dec 2013 14:13:20 -0800

David Hoaglin makes a good point.  In addition, you might look at:

Raftery, Adrian E.  1995.  "Bayesian Model Selection in Social
Research." *Sociological Methodology* 25: 111-163.

He has a nice discussion of what you might regard as cut-offs for a
meaningful difference between models.  At the risk of undermining
incentive to read Raftery's very informative paper, check out, for
example, Tables 8 and 9 on page 141. The text further discusses what
counts as weak, strong, and very strong evidence in a comparison of
models.

Take care
Sam

On Sat, Dec 14, 2013 at 2:03 PM, David Hoaglin <dchoaglin@gmail.com> wrote:
> Hi, Julie.
>
> I'm not sure what you mean by "imbedded."
>
> AIC is not an absolute measure.  If you have a set of models that
> should be good models on substantive grounds, you can choose the model
> that has the smallest AIC.  Those models do not need to be nested, and
> they do not have to be based on the same family of distributions (as
> long as the calculation of AIC includes all the constants in the
> likelihood).  The usual definition of AIC applies to large samples.
> If the ratio of the sample size (n) to the total number of parameters
> (K) is not "large," say n/K < 40, it is better to use a modified
> version of AIC.
>
> Are you able to use deviance (and DIC) to compare each of your models
> against the corresponding "saturated" model?
>
> Also, Agresti (2010) discusses ways of assessing goodness of fit.
>
> David Hoaglin
>
> Agresti, A. (2010).  Analysis of Ordinal Categorical Data, 2nd ed.
> John Wiley & Sons.
>
>
> On Sat, Dec 14, 2013 at 2:48 PM, Julie Lamoureux <drjuliel@comcast.net> wrote:
>> Good day,
>>
>> I am looking for "normed" goodness of fit indices for ordered logistic
>> regression.  We submitted a manuscript with the results of 2 OLR (on two
>> different unrelated outcomes) and the reviewer is asking how we assessed
>> model goodness of fit and its usefulness.  I know AIC and BIC let us compare
>> models that are "imbedded" but can we use AIC and BIC to determine how
>> useful the model is?  If so, what "cut-off" for those indices can be
>> considered "good"?  Is there something else I am not aware of to answer this
>> question?
>>
>> Thank you for your time
>>
>> Julie Lamoureux
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