Composite Index Construction with Expert Opinion

Chen, Rong; Ji, Yuanyuan; Guo-lin, Jiang; Xiao, Han; Xie, Ruoqing; Zhu, Pingfang

doi:10.1080/07350015.2021.2000418

Cited by 10 publications

(5 citation statements)

References 36 publications

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“…The combination weight (

) is obtained through PCA and computed for k candidate indices (

) where

= 1 , …, K with N observations (

= 1 , …, N ) [ 32 ]. The attached weights (

) are normalized such that

[ 74 ]. Furthermore, the variances of the principal components are demonstrated through eigenvectors.…”

Section: Resultsmentioning

confidence: 99%

The effect of institutional quality on agricultural value added in East Africa

Gelgo,

Gemechu,

Bedemo

2023

Heliyon

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“…The combination weight (

) is obtained through PCA and computed for k candidate indices (

) where

= 1 , …, K with N observations (

= 1 , …, N ) [ 32 ]. The attached weights (

) are normalized such that

[ 74 ]. Furthermore, the variances of the principal components are demonstrated through eigenvectors.…”

Section: Resultsmentioning

confidence: 99%

The effect of institutional quality on agricultural value added in East Africa

Gelgo,

Gemechu,

Bedemo

2023

Heliyon

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“…The core of SAW-Max-Entropy lies in its hybrid weighting scheme, which considers subjective information while objectively defining the weights. Hybrid weighting schemes, wherein expert opinion is considered in the objective definition of weights, are frequently used in several studies [49][50][51][52].…”

Section: Saw-max-entropy Methodsmentioning

confidence: 99%

“…The fundamental difference between SAW-Max-Entropy and the previously presented methods lies in the objective of maximization. While other methods aim to maximize scores of the composite indicator [28], the variance extracted from the sub-indicators [49], the composite indicator external validity [50], and the composite indicator spatial autocorrelation [53], SAW-Max-Entropy aims to maximize informational diversity (Entropy Index), specifically focusing on enhancing the discriminating power of the composite indicator.…”

Section: Saw-max-entropy Methodsmentioning

confidence: 99%

The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators

Libório,

Karagiannis,

Diniz

et al. 2024

Entropy

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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.

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“…Based on a review of reference literature concerning synthetic measures (OECD, 2008;Mazziotta & Pareto, 2013, pp. 67-80;Godlewska & Sidorczuk-Pietraszko, 2019;Małkowska et al, 2021;Chen et al, 2021), the following research procedure stages were adopted for the purposes of this study: 1. Defining the objective and subjective scope of research; 2.…”

Section: Methodsmentioning

confidence: 99%

Economic competitiveness vs. green competitiveness of agriculture in the European Union countries

Nowak

Kasztelan

2022

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Research background: Many scientists have researched the economic competitiveness of agriculture. At the same time, considerably less attention is paid to the so-called green competitiveness. Considering a global trend searching for solutions to reduce the environmental impact of the agricultural sector, it seems reasonable to explore the overlap between economic competitiveness and green competitiveness. Purpose of the article: This study aims to answer the following questions: What is the level of economic and green competitiveness of agriculture in respective countries? What is the level of the comprehensive competitiveness of agriculture in EU member states? Do the economic competitiveness outcomes of respective countries coincide with their green competitiveness rankings? Methods: Taxonomic methods were applied to design synthetic indices of economic, green and comprehensive competitiveness of 27 member states of the European Union, based on multi-criteria sets of specific indicators from 2018. Findings & value added: The results of analyses imply that, in general, the level of green competitiveness of agriculture is higher than the level of its economic competitiveness in EU member states. Simultaneously, the developed rankings show that respective countries' economic and green competitiveness are not linked. In other words, economic competitiveness outcomes do not match green competitiveness outcomes for EU agriculture. This work is a genuine contribution to studies on the methods for measuring and evaluating the competitiveness of agriculture as it designs separate synthetic measures for economic and green competitiveness and confronts both types of competitiveness in EU member states. The research findings for the first time provide clear answers to questions about the mutual relationship between economic and green competitiveness in agriculture. Furthermore, an added value of this study is that it introduces and attempts to define the notion of green competitiveness.

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Composite Index Construction with Expert Opinion

Cited by 10 publications

References 36 publications

The effect of institutional quality on agricultural value added in East Africa

The effect of institutional quality on agricultural value added in East Africa

The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators

Economic competitiveness vs. green competitiveness of agriculture in the European Union countries

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