This research offers a solution to a highly recognized and controversial problem within the composite indicator literature: sub-indicators weighting. The research proposes a novel hybrid weighting method that maximizes the discriminating power of the composite indicator with objectively defined weights. It considers the experts’ uncertainty concerning the conceptual importance of sub-indicators in the multidimensional phenomenon, setting maximum and minimum weights (constraints) in the optimization function. The hybrid weighting scheme, known as the SAW-Max-Entropy method, avoids attributing weights that are incompatible with the multidimensional phenomenon’s theoretical framework. At the same time, it reduces the influence of assessment errors and judgment biases on composite indicator scores. The research results show that the SAW-Max-Entropy weighting scheme achieves greater discriminating power than weighting schemes based on the Entropy Index, Expert Opinion, and Equal Weights. The SAW-Max-Entropy method has high application potential due to the increasing use of composite indicators across diverse areas of knowledge. Additionally, the method represents a robust response to the challenge of constructing composite indicators with superior discriminating power.

This research explores the relationship between the implicit importance of the variables of a multidimensional phenomenon within the context of urban inequalities and the weights attributed to these variables during the process of building a composite indicator (CI) by data-based weighting methods, such as principal component analysis (PCA). The objective is to test whether a CI can be statistically consistent or even adequate to represent the phenomenon but, at the same time, be composed of variables with weights that do not adhere to reality and, consequently, transmit false information, hide problems, and lead to wrong policies. This hypothesis is tested in a study of the intraurban inequality of a Brazilian urban conurbation. The results show a CI that is internally (Cronbach’s α = 0.80) and externally (Pearson’s correlation coefficient = 0.64) consistent but that captures only 20 percent of the information related to the phenomenon and is unable to completely represent its dimensions or account for important variables. The results suggest that the external validation of CI based on known indicators might not be a good strategy and that qualitative indicators can be useful to verify the extent to which a CI is suitable to represent a phenomenon.