The use of Poisson regression in the sociological study of suicide

Abstract

This paper explains how Poisson regression can be used in studies in which the dependent variable describes the number of occurrences of some rare event such as suicide. After pointing out why ordinary linear regression is inappropriate for treating dependent variables of this sort, we go on to present the basic Poisson regression model and show how it fits in the broad class of generalized linear models. Then we turn to discussing a major problem of Poisson regression known as overdispersion and suggest possible solutions, including the correction of standard errors and negative binomial regression. The paper ends with a detailed empirical example, drawn from our own research on suicide. In social research, we often encounter dependent variables that describe the frequency of occurrence of events of various kinds. Students of deviant behavior, for example, look at whether media reporting of celebrity suicides leads to an increase in the number of self-destruction; demographers study the impact of environmental hazards on the number of birth defects; and sociologists of science examine the factors that influence the frequency with which publications are cited by fellow researchers. In cases like these, scholars frequently rely on ordinary linear regression to analyze their data. This sometimes produces acceptable results, especially when the mean frequency of occurrence of the event under study is relatively large, since in this situation the distribution of the dependent variable does not usually deviate substantially from the normal distribution assumed by ordinary least