# I want to make sure the use of Poisson count model OR negative binomial model is right for my study

I have a planned study to conduct in few weeks. As it is my first time to deal with a 'count' dependent variable, I have been searching on this website what statistics tools I should use.

I have two independent variables - both binary (0 or 1).

The main dependent variable is a count variable that range from 0 to 3. To be more specific, participants rank 6 independent options that are given. If they have all three options - Option A, Option B, and Option C - in the highest rank of three, I assign them with a score of 3. If they have only two of them in the highest rank, I assign, 2, and so on until 0.

From what I read from this website, it seems both "poisson count model" and "negative binomial model" can be used for my study.

I am familiar with the ordinary linear regressions but not with either of the two. I believe that the Poisson model would be a better choice, since I have read that Negative binomial is used for more 'outstretched' data.

I would like to hear your thoughts on this!

• Please edit the question to say more about what the "count" outcomes represent. Might that be the number of "correct" outcomes out of 3 trials, or a ranking of something on an integer scale from 0 to 3, or something else? The way to proceed can depend on the specific process that leads to your "count" outcome. Neither Poisson nor negative binomial might end up the best choice. Please edit your question to provide that information, as comments are easy to overlook and can be deleted.
– EdM
Commented Mar 11, 2022 at 13:39
• Thanks, @EdM! Thats a good point. I just edited to make the description of my dependent variable more precise. Thanks for letting me know to make direct edits to the content - I am new here, so I would have gone for just leaving comments if not your comment!
– Ted
Commented Mar 12, 2022 at 23:51
• This appears to be an ideal situation for an ordinal semiparametric model. See here for resources. Commented Mar 13, 2022 at 14:36
• @FrankHarrell Thanks for your input! I will look into this more!
– Ted
Commented Mar 22, 2022 at 21:24