title: Overdispersion tests in count-data analysis.
abstract: Count data are commonly assumed to have a Poisson distribution, especially when there is no diagnostic procedure for checking this assumption. However, count data rarely fit the restrictive assumptions of the Poisson distribution. The violation of much of such assumptions commonly results in overdispersion, which invalidates the Poisson distribution. Undetected overdispersion may entail important misleading inferences, so its detection is essential. In this study, different overdispersion diagnostic tests are evaluated through two simulation studies. In Exp. 1, the nominal error rate is compared under different sample sizes and lamda conditions. Analysis shows a remarkable performance of the chi2 df test. In Exp. 2 and 3, statistical power is compared under different sample sizes, lamda, and overdispersion conditions. Chi2 and LR tests provide the highest statistical power.
authors.affiliation: Departament de Psicobiologia i de Metodologia de les CC. de la Salut, Facultat de Psicologia, Edifici B. Campus de la Universitat Autònoma de Barcelona, 08193-Cerdanyola del Vallès, Spain. Jaume.Vives@UAB.es
authors.forename: Jaume
authors.initials: J
authors.lastname: Vives
authors.forename: Josep-Maria
authors.initials: JM
authors.lastname: Losilla
authors.forename: Maria-Florencia
authors.initials: MF
authors.lastname: Rodrigo
authors.forename: Mariona
authors.initials: M
authors.lastname: Portell
mesh_terms: D006801:Humans; D008960:Models, Psychological; D011584:Psychology / Q000706:statistics & numerical data*