Quantitative Storytelling in the Making of a Composite Indicator

Kuc‐Czarnecka, Marta; Piano, Samuele Lo; Saltelli, Andrea

doi:10.1007/s11205-020-02276-0

Cited by 64 publications

(37 citation statements)

References 83 publications

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“…However, it is possible to state that CIs are a mathematical aggregation of variables, normalized or standardized, weighted or not, in a single indicator capable of representing different dimensions of a complex concept or phenomenon [33][34][35]. There is strong criticism about the aggregation and weighting process for the construction of a CI [36,37], as well as about its ability to measure a complex concept or phenomenon [38]. Even so, CIs have attracted the attention of researchers in an increasing number of publications on varied areas of knowledge [38], including the analysis of intra-urban inequality [1].…”

Section: Composite Indicatorsmentioning

confidence: 99%

Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

et al. 2020

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Composite indicators are almost always determined by methods that aggregate a reasonable number of manifest variables that can be weighted—or not—as new synthesis variables. A problem arises when these aggregations and weightings do not capture the possible effects that the various underlying dimensions of the phenomenon have on each other, and consequently distort the assessment of intra-urban inequality. In this paper, we explore the direct and indirect effects that the different underlying dimensions of intra-urban inequality have on indicators that represent this phenomenon. Structural equation modeling was used to build a composite indicator that captures the direct and indirect effects of the underlying dimensions of intra-urban inequality. From this modeling that combines confirmatory factor analysis with a system of simultaneous equations, the intra-urban inequality of the urban conurbation of Maringá–Sarandi–Paiçandu, Brazil was measured. The model comprises first- and second-order structures. The first-order structure is composed of non-observed variables that represent three underlying dimensions of intra-urban inequality. The second-order structure is the intra-urban inequality composite indicator that synthesizes the non-observed variables of the first-order structure. The model aims at demonstrating how to perform a theorized measurement of urban inequality so that it makes it possible to identify which dimensions most influence the others, as well as which dimensions are more relevant to this purpose.

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Section: Composite Indicatorsmentioning

confidence: 99%

Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

et al. 2020

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“…Another way to assess indicator reliability involves comparing the order of the results obtained. To do this, and based on the classifications obtained by the regions in the three indicators, we estimated rank correlation coefficients, following the methodological guidelines also applied in other works of Silva et al and Kuc-Czarnecka et al [76,77]. In all instances, coefficients are high (>0.70) and statistically significant, thus indicating that the variability in the positions each region occupies in the ranking in accordance with the weighting criterion is fairly negligible and that this therefore has little relevance when interpreting the final result.…”

Section: Ranking Of Regions and Reliability Of The Eircmentioning

confidence: 99%

Reliability of Creative Composite Indicators with Territorial Specification in the EU

Miguel

Prieto

2020

Sustainability

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There is wide consensus regarding the capacity of the creative economy as a factor in economic development and sustainability in regions in the medium term, and it is an issue that has aroused interest in measuring the creative potential of areas by estimating composite indicators. Most works posit indicators by countries or cities and normally employ a single aggregation method. This work proposes the construction of a synthetic indicator of creativity with regional disaggregation for a wide sample of European countries, comparing different aggregation methods under the hypothesis of the benefit of the doubt (BoD). We use Eurostat data and also analyse spatial dependence processes. Findings point to an inequality in the regional distribution of creativity in Europe and this reveals creative gap models in areas, suggesting that creativity triggers greater spatial disparities wherever it is found. The principal creative clusters are located in regions in central and northern Europe, particularly in regions with national capitals. The regional distribution of the indicator evidences positive spatial dependence, thereby revealing the existence of spatial externalities that provide feedback for the concentration process and, therefore, a widening of the disparities.

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“…True to its nature, a composite indicator is usually built to 'tell a story'. It is thus ideally suited to identify and bring attention to a possibly latent phenomenon (Kuc-Czarnecka et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The use of composite indicators has increased in the study of human well-being and progress, including a wide range of topics (Becker et al 2017;Kuc-Czarnecka et al 2020). One of the fields in which they have had great relevance is in the study of job quality (Ghai 2002;Bonnet et al 2003;Anker et al 2003;Bescond et al 2003;Tangian 2007; represent new challenges for the quality of jobs in advanced industrialised societies.…”

Section: Introductionmentioning

confidence: 99%

New Model for Measuring Job Quality: Developing an European Intrinsic Job Quality Index (EIJQI)

Mira

2021

Soc Indic Res

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This article deals with the development of a new model for measuring job quality based on the intrinsic components of work, an European Intrinsic Job Quality Index. The objective is measure job quality on the basis of the characteristics inherent to the labour activity itself, and not from its financial rewards. First, we review the existing literature on current measurement models and justify the need for an index of this nature. Secondly, we explain the fundamental methodological decisions adopted for the construction of the index, and present the descriptive model, the indicators that make up each dimension, and the empirical model. Finally, we present the index scores by European Union countries within the framework of institutional theories. In this work, we have used a quantitative methodology, based on social indicator systems, and it has been carried out mainly with data from the European Working Conditions Survey (2015). The fundamental contribution of this article is the construction of a new model for measuring the quality of work, robust, valid and reliable, which will allow us to monitor the intrinsic job quality of the member countries, and thus provide relevant information that contributes to the framework of public policies.

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Quantitative Storytelling in the Making of a Composite Indicator

Cited by 64 publications

References 83 publications

Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

Reliability of Creative Composite Indicators with Territorial Specification in the EU

New Model for Measuring Job Quality: Developing an European Intrinsic Job Quality Index (EIJQI)

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