Quantifying uncertainty in ranking problems with composite indicators: a Bayesian approach

摘要

Purpose - The purpose of this paper is to present an inductive methodology, which supportsrnranking of entities. Methodology is based on Bayesian latent variable measurement modeling andrnmakes use of assessment across composite indicators to assess internal and external model validityrn(uncertainty is used in lieu of validity). Proposed methodology is generic and it is demonstrated on arnwell-known data set, related to the relative position of a country in a "doing business."rnDesign/methodology/approach - The methodology is demonstrated using data from the WorldrnBanks' "Doing Business 2008" project. A Bayesian latent variable measurement model is developedrnand both internal and external model uncertainties are considered.rnFindings - The methodology enables the quantification of model structure uncertainty throughrncomparisons among competing models, nested or non-nested using both an information theoreticrnapproach and a Bayesian approach. Furthermore, it estimates the degree of uncertainty in the rankingsrnof alternatives.rnResearch limitations/implications - Analyses are restricted to first-order Bayesian measurementrnmodels.rnOriginality/value - Overall, the presented methodology contributes to a better understanding ofrnranking efforts providing a useful tool for those who publish rankings to gain greater insights into thernnature of the distinctions they disseminate.