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Proposal for a Dynamic Composite Indicator
by
Landaluce-Calvo, M. Isabel
, Gozalo-Delgado, Mariola
in
Clustering
/ Comparative analysis
/ Conditioning
/ Discriminant analysis
/ Exploratory factor analysis
/ Factor analysis
/ Human Geography
/ Matrices
/ Measurement
/ Microeconomics
/ ORIGINAL RESEARCH
/ Public Health
/ Quality of Life Research
/ Social Sciences
/ Sociology
/ Statistical analysis
/ Trend Analysis
/ Trends
2021
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Proposal for a Dynamic Composite Indicator
by
Landaluce-Calvo, M. Isabel
, Gozalo-Delgado, Mariola
in
Clustering
/ Comparative analysis
/ Conditioning
/ Discriminant analysis
/ Exploratory factor analysis
/ Factor analysis
/ Human Geography
/ Matrices
/ Measurement
/ Microeconomics
/ ORIGINAL RESEARCH
/ Public Health
/ Quality of Life Research
/ Social Sciences
/ Sociology
/ Statistical analysis
/ Trend Analysis
/ Trends
2021
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Do you wish to request the book?
Proposal for a Dynamic Composite Indicator
by
Landaluce-Calvo, M. Isabel
, Gozalo-Delgado, Mariola
in
Clustering
/ Comparative analysis
/ Conditioning
/ Discriminant analysis
/ Exploratory factor analysis
/ Factor analysis
/ Human Geography
/ Matrices
/ Measurement
/ Microeconomics
/ ORIGINAL RESEARCH
/ Public Health
/ Quality of Life Research
/ Social Sciences
/ Sociology
/ Statistical analysis
/ Trend Analysis
/ Trends
2021
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Journal Article
Proposal for a Dynamic Composite Indicator
2021
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Overview
This paper contributes, with a dynamic approach, to the research on the creation of comparable composite indicators by presenting a proposal for an exploratory factor analysis protocol to enable a comparative trend analysis. The originality of the study lies in the three dimensions of information for analysis: observations, variables and units of time. The proposal involves various stages of analysis with the ultimate, albeit not exclusive, aim of obtaining what is known as a Global Dynamic Indicator. The analysis process begins by structuring the data into a three-dimensional global matrix, thereby conditioning, while also, and primarily, enriching the later stages. A combination of multiple factor analysis and a clustering technique is the selected approach for successfully meeting the challenges involved. The appropriateness and versatility of the proposal are validated through the analysis of the trends of the EU member states towards the targets set by the 2020 Strategy. The study period runs from 2009 to 2018. The empirical work enables the visualisation and quantification of trend differences and similarities across member states collectively and individually, and across all the variables and years selected for analysis. The relevant findings will be quantified by means of a synthetic indicator for each unit of time and a global indicator for the period as a whole. Some of the conclusions reached by this paper are consistent with those already published by various authors.
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