Re: different result in two nb reg and pisson reg

Posted by Andy W on Sep 15, 2014; 5:36pm
URL: http://spssx-discussion.165.s1.nabble.com/different-result-in-two-nb-reg-and-pisson-reg-tp5727252p5727261.html

The increases in the standard errors are larger than I might guess, but off-hand they don't suggest anything inherently strange going on in the estimated models.

A few notes:

 - You have warnings about convergence for both models in the Model Effects table.
 - The intercept for all of the models shown is really large. The variable "time.travel" is coded in time format, so I suspect SPSS is predicting time travel in seconds. May be best to physically change these to integers representing minutes. (This might also help SPSS converge to a solution in the model effects tables)
 - In the negative binomial model you restrict the dispersion parameter to be equal to 1. Most situations I would imagine you want to estimate it from the data, e.g. "DISTRIBUTION=NEGBIN(MLE)"

More generally:

 I can't tell from this data whether you should be fitting a Poisson model at all! It is quite possible a better fitting model is to simply take the log of minutes - if time.travel needs to be transformed at all. You have a restricted range of time.travel from 5 minutes to 4.5 hours, suggesting that a Poisson or Neg Bin model may substantially deviate from the observations -- especially in the tails. [Both will assign mass to the [0 to <= 5] minute range, but with a large mean estimate the mass will be very small.]

Try looking at a histogram of travel time and see you can draw a reasonable fit line for Poisson or a Negative Binomial Model over top of it. [Given no times are below 5 minutes I'm tempted to suggest looking at censored models, but it is speculation given the limited info. you've provided.]

The variance in the DESCRIPTIVES is somewhat misleading for the same time format reason I previously stated. You also have no point mass at 0, so there is nothing wrong off hand in taking the logs (if the log of time travel theoretically makes sense.)

More could said about the models estimated (e.g. it appears you use SPLIT FILE, but you may consider stacking the models and estimating them + interactions all at once). [Also it isn't clear the nature of the categories of time and how they relate to one another.] But hopefully this is enough to chew on for a bit ;)