On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness

Abstract

In recent times, composite indicators have gained astounding popularity in a wide variety of research areas. Their adoption by global institutions has further captured the attention of the media and policymakers around the globe, and their number of applications has surged ever since. This increase in their popularity has solicited a plethora of methodological contributions in response to the substantial criticism surrounding their underlying framework. In this paper, we put composite indicators under the spotlight, examining the wide variety of methodological approaches in existence. In this way, we offer a more recent outlook on the advances made in this field over the past years. Despite the large sequence of steps required in the construction of composite indicators, we focus particularly on two of them, namely weighting and aggregation. We find that these are where the paramount criticism appears and where a promising future lies. Finally, we review the last step of the robustness analysis that follows their construction, to which less attention has been paid despite its importance. Overall, this study aims to provide both academics and practitioners in the field of composite indices with a synopsis of the choices available alongside their recent advances.

As simple as possible but not simpler in Multiple Criteria Decision Aiding: the robust-stochastic level dependent Choquet integral approach

“…We believe that the good properties of the methodology we have presented can be very useful in many real life complex decision problems in the main domains of interest such as sustainable development [1], wellbeing and happiness economics [19,20], ranking of universities [11], country competitiveness [10], and so on.…”

“…If E = {i, j} and µ L is a 2-additive interval level dependent capacity, then (19) can be rewritten as…”

“…Considering the tail of this ranking, only six universities cannot be in the last position. Moreover, the universities being most frequently the last are U 10 (b 19 Analogously, to pairwise compare the considered universities, we computed the pairwise winning indices shown in Table 6. Considering the three universities being most frequently in the first position, that are U 16 , U 5 and U 11 , one can observe that U 16 is preferred to U 5 and U 11 with frequencies of 82.043% and 87.263%, respectively.…”

Hydrologic Resilience from Summertime Fog and Recharge: A Case Study for Coho Salmon Recovery Planning

“…We address the relationship of FLCC to stream temperature more fully in the Limitations and Future Work section. To generate matrices to compare the HC across CPUsWs and PSAs for two different use-case scenarios, we combined FLCC and recharge indices into composite HC indices without imposing a differential weighting scheme (Greco et al 2019). Many categorical breaks could be used to classify the continuous variables into three-bin indices.…”

Sensitivity analysis of travel and tourism competitiveness Index to changes in component weighting

“…When aggregating indicators into a composite index, weights can be assigned according to the adopted theoretical framework, expert opinion, the results of factor analysis (in case of using sets of heterogeneous variables), correlation with the dependent variable (Saisana & Tarantola, 2002). Their goal is that the weight of the weights corresponds to the relative importance of each variable (or group of variables or components) in the composite index (Greco et al, 2019). The weights have an important influence on the values of the composite indices and therefore the weighting system must be explicit and transparent.…”