Preelectoral polls variability: A hierarchical Bayesian model to assess the role of house effects with application to Italian elections

Abstract

It is widely known that preelectoral polls often suffer from nonsampling errors that pollsters try to compensate for in final estimates by means of diverse ad hoc adjustments, thus leading to well-known house effects. We propose a Bayesian hierarchical model to investigate the role of house effects on the total variability of predictions. To illustrate the model, data from preelectoral polls in Italy in 2006, 2008 and 2013 are considered. Unlike alternative techniques or models, our proposal leads: (i) to correctly decompose the different sources of variability; (ii) to recognize the role of house effects; (iii) to evaluate its dynamics, showing that variability of house effects across pollsters diminishes as the date of election approaches; (iv) to investigate the relationship between house effects and overall prediction errors.