1995/11/01 by William Gardner, Edward P. Mulvey, Esther Shaw +1 · 5 citations
Arts and Humanities · Mathematics · Psychology · #Behavioral and Psychological Studies #Binomial regression #Count data #Cross-sectional regression #Diverse Music Education Insights #Econometrics #Generalized linear model #Linear regression #Mathematics #Mental Health Research Topics #Negative binomial distribution #Overdispersion #Poisson distribution #Poisson regression #Polynomial regression #Population #Proper linear model #Quasi-likelihood #Regression #Regression analysis #Regression diagnostic #Statistical Methods and Bayesian Inference #Statistics #Zero-inflated model
paper · doi:10.1037/0033-2909.118.3.392
openalex publication_date 1995/11/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/31
The regression models appropriate for counted data have seen little use in psychology. This article describes problems that occur when ordinary linear regression is used to analyze count data and presents 3 alternative regression models. The simplest, the Poisson regression model, is likely to be misleading unless restrictive assumptions are met because 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 estimated that corrects the regression model's inferential statistics. In the second alternative, the negative binomial regression model, a random term reflecting unexplained between-subject differences is included in the regression model. The authors compare the advantages of these approaches.