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 Rachel Roiland posted on Thursday, March 15, 2012 - 10:06 am
Hello Drs. Muthen,
I am trying to understand how one interprets the negative binomial dispersion parameter in my output. I just ran a very simple model (i.e., one count variable dependent variable regressed on to two continuous varibles) and received a dispersion parameter estimate of 0.114 and a two - tailed p-value of 0.461. I am familiar with the alpha dispersion estimate you receive from STAT and SAS and if the parameter isn't statistically significant, dispersion is not an issue and you can use a Poisson model. Can the parameter estimate provided in MPLUS output be interpreted in the same way?
Thank you!
 Bengt O. Muthen posted on Friday, March 16, 2012 - 8:40 am
Yes.
 Bengt O. Muthen posted on Friday, March 16, 2012 - 9:25 am
With the caveat of the problem of testing a parameter at the border of its admissible space (zero). I use BIC instead to choose between the various count models. See the Marital Affairs example on slides 39-41 in our Topic 5 handout on the website, showing the many variations on the count modeling theme available in Mplus.
 Rachel Roiland posted on Monday, March 26, 2012 - 6:55 am
Thanks so much!
 Lisa M. Yarnell posted on Tuesday, March 26, 2013 - 10:50 pm
Hi Bengt and Linda,

Is there a rule of thumb for how large the dispersion statistic should be in ZINB regression? Is it supposed to be close to 1.00 for the NB to be appropriate?

Or should I just be judging its significance, as stated above, as a reflection of whether NB is preferable over Poisson?

Tnanks.
 Linda K. Muthen posted on Wednesday, March 27, 2013 - 2:31 pm
It should be greater than zero. You can check the following reference for a maximum value:

Hilbe, J. M. (2007). Negative binomial regression. Cambridge,
UK: Cambridge University Press.

You can look at significance or you can compare the BIC's from the models with and without dispersion.
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