Applied Regression Models in the Social Sciences

In this chapter we cover the modeling of a dependent variable that is neither continuous nor categorical, but is a count of the number of events. Dependent count variables measure the number of times an event has occurred. An example from demography is the number of children ever born to a woman or man in their lifetimes. Frequently, count variables are treated as though they are continuous and unbounded, and ordinary least squares (OLS) models are used to estimate the effects of independent variables on their occurrence. But if the OLS assumptions we discussed in Chapter 8 are not met, then the use of OLS for count outcomes may result in inefficient, inconsistent, and biased estimates. There are many kinds or classes of models that may be used to estimate count dependent variables. In this chapter we consider five models: (1) the Poisson regression model; (2) the negative binomial regression model; (3) the zero-inflated count model; (4) the zero-truncated count model; and (5) the hurdle regression model.