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.

这项研究探索了在城市不平等的背景下多维现象变量的隐含重要性与通过基于数据的加权方法（例如本金）建立复合指标（CI）的过程中归因于这些变量的权重之间的关系。成分分析（PCA）。目的是测试CI是否可以在统计上一致，甚至足以表示现象，但同时由权重不符合实际情况的变量组成，从而传递虚假信息，隐藏问题以及导致错误的政策。在对巴西城市住宅的城市内部不平等的研究中检验了这一假设。结果显示内部（Cronbach'sα= 0.80）和外部（Pearson's相关系数= 0）的CI。64）一致，但仅捕获与该现象有关的信息的20％，并且无法完全表示其维度或无法解释重要变量。结果表明，基于已知指标对CI进行外部验证可能不是一个好的策略，而定性指标可用于验证CI适于代表某种现象的程度。