Re: Negative Binomial Regression

Posted by Ryan on Nov 11, 2011; 5:05am
URL: http://spssx-discussion.165.s1.nabble.com/Negative-Binomial-Regression-tp4980662p4983448.html

Kathryn,

Lots could be said but before we get too far down this road, exactly what is your response variable (e.g.,  # of times or days purged) and is there an absolute upper limit. Also, what does the shape of the distribution look like for the non-zero values? Another question, could it be argued that everybody in your sample is "at risk" of scoring a 1 or higher?

Ryan

On Nov 10, 2011, at 5:24 AM, Kathryn Gardner <[hidden email]> wrote:

Dear List,
I am using negative binomial regression and would appreciate some input on how to run and interpret the analysis. Excuse my ignorance, but I am struggling to find information:

Running the analysis:
1) Under "type of model", what's the difference in selecting the "negative binomial with log link" model with the parameter fixed at 1, or a custom model where the paramater is estimated? Which is the correct option and under what circumstances?
2) I tried both of the options above, and in  both cases the deviance/df ratio was below 1, suggesting underdispersion I believe. I read something in the context of Poission regression that suggests this can be resolved by calculating the scale as the inverse of the Deviance/df: Compute pscale=1/2.2033  then refitting the model using pscale as the "Scale Weight Variable" under the Response tab. Can this be used in Negative Binomial regression also? And if so, what does this do exactly?
3) I wondered whether a zero inflated negative binomial regression might be more appropriate but i'm not sure. My response is an eating disorder variable, and 198 out of 235 participants have scored 0 to indicate they do not engage in purging behaviours such as vomiting, and the remainder of the sample have scored between 1 and 21 (Mean = .71, SD = 2.51). If a zero inflated model is more appropriate, how do I run this in SPSS?

Interpreting the output:
1) Is there any guidance on how small deviance should be to indicate good model fit?
2) The regression coefficients in the output appear to be unstandardised in the output. Is there a way to produce standardised estimates other than standardising variables beforehand?

Many thanks.
Kathryn