REGRESSION-ANALYSES OF COUNTS AND RATES - POISSON, OVERDISPERSED POISSON, AND NEGATIVE BINOMIAL MODELS

The regression models appropriate for counted data have seen little us e in psychology. This article describes problems that occur when ordin ary linear regression is used to analyze count data and presents 3 alt ernative regression models. The simplest, the Poisson regression model , is likely to be misleading unless restrictive assumptions are met be cause individual counts are usually more variable (''overdispersed'') than is implied by the model. This model can be modified in 2 ways to accomodate this problem. In the overdispersed model, a factor can be e stimated that corrects the regression model's inferential statistics. In the second alternative, the negative binomial regression model, a r andom term reflecting unexplained between-subject differences is inclu ded in the regression model. The authors compare the advantages of the se approaches.