Hello,
I have received contradictory suggestions on how to analyze certain data and would love to get some feedback.
My dependent variable is the number of bills initiated by each legislator that was approved by the chamber (my independent variables are a series of legislator traits and contextual features). I also have data on the total number of bills each legislator initiated.
The suggestions I received were:
1) The data appears to be grouped binary data. Use GLM family (binomial) link(logit), with the number of approved bills as the dependent variable and the total number of bills initiated by the legislator as the number of trails.
2) Use fractional logistic regression. Use the number of bills approved and the total number of bills initiated to create a percentage approved variable and use that as the dependent variable and run a Fracreg logit regression.
3) Use a count model. Poisson or NBREG with the total number of bills passed as the dependent variable and include the total number of initiated variables as an independent variable.
What model appears to be more appropriate?
I have a lot of zeros (half of the legislators who initiated bills don't have any that was approved). Should this affect my choice of model? (e.g. zero-inflated count or beta regression)
Thank you,
Eduardo
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