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RE: st: statistical significance of cut points in ordered logit


From   Grace Jessie <gracejessie@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: statistical significance of cut points in ordered logit
Date   Mon, 17 May 2010 18:01:47 +0000

Richard and Martin,
thank you for reply and help.
Richard, thanks a lot for your elaborate explanation, which helps me much.
 
Regards,
Grace


----------------------------------------
> Date: Mon, 17 May 2010 11:30:23 -0400
> To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu
> From: richardwilliams.ndu@gmail.com
> Subject: RE: st: statistical significance of cut points in ordered logit
>
> At 08:43 AM 5/17/2010, Grace Jessie wrote:
>>Additionally, does the statistical significance of cut points in
>>ordered logit matter?
>>I found there are no z or P>|z| for cut points, though I could get it.
>
> If you want p values for the cutpoints, you can get them with -oglm-,
> available from SSC. If no options are specified, oglm produces the
> same estimates as ologit (albeit more slowly).
>
> I'm not sure what you do with the p values once you have
> them. Indeed, there is no reason that one of the cutpoints can't be
> zero. I suppose if the confidence intervals for two cut points
> overlapped, you might want to examine whether the N is too small in
> some categories (of course you may want to examine that
> regardless). If Ns in some categories are very small, you might want
> to combine some adjacent categories. For example, I don't like using
> the auto data for ordinal regression problems because the Ns are way
> too small both overall and for individual categories of rep77, e.g.
>
> webuse fullauto
> ologit rep77 foreign length mpg
> tab1 rep77
>
> The latter command yields
>
> -> tabulation of rep77
>
> Repair |
> Record 1977 | Freq. Percent Cum.
> ------------+-----------------------------------
> Poor | 3 4.55 4.55
> Fair | 11 16.67 21.21
> Average | 27 40.91 62.12
> Good | 20 30.30 92.42
> Excellent | 5 7.58 100.00
> ------------+-----------------------------------
> Total | 66 100.00
>
> You are supposed to have at least 100 cases for maximum likelihood
> methods, and I would think even more for something like ologit. Toss
> in the extremely small Ns at the 2 extremes and I don't know how much
> you can trust these estimates.
>
> Incidentally, if you now run the -brant- command (part of Long &
> Freese's spost package) you get
>
> . brant
> not all independent variables can be retained in all binary logits
> brant test cannot be computed
> r(999);
>
> So again, my big concern is a small overall N and/or small
> subcategory Ns. Cutpoints that don't significantly differ from each
> other might be a result of such problems, but there are other ways to
> check too, e.g. just look at your frequencies and/or run a brant test.
>
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> HOME: (574)289-5227
> EMAIL: Richard.A.Williams.5@ND.Edu
> WWW: http://www.nd.edu/~rwilliam
>
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