Does uncertainty in single indicators affect the reliability of composite indexes? An application to the measurement of environmental performances of Italian regions

In recent decades, the measurement and evaluation of important social and natural phenomena has significantly evolved, with many traditional measurements based on single variables increasingly being replaced by multidimensional approaches. One key aspect of these approaches is the development of com...
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Gespeichert in:

Autor*in:	Vincenzo Mauro [verfasserIn] Caterina Giusti [verfasserIn] Stefano Marchetti [verfasserIn] Monica Pratesi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	62-08 62P12 62P25

Übergeordnetes Werk:	In: Ecological Indicators - Elsevier, 2021, 127(2021), Seite 107740-
Übergeordnetes Werk:	volume:127 ; year:2021 ; pages:107740-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.ecolind.2021.107740

Katalog-ID:	DOAJ051313588

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spelling	10.1016/j.ecolind.2021.107740 doi (DE-627)DOAJ051313588 (DE-599)DOAJca84ceda1d7345619d1dbf7731769e7c DE-627 ger DE-627 rakwb eng QH540-549.5 Vincenzo Mauro verfasserin aut Does uncertainty in single indicators affect the reliability of composite indexes? An application to the measurement of environmental performances of Italian regions 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent decades, the measurement and evaluation of important social and natural phenomena has significantly evolved, with many traditional measurements based on single variables increasingly being replaced by multidimensional approaches. One key aspect of these approaches is the development of composite indexes, usually real-value functions of multiple achievements of a group of units. The achievements in each of the selected dimensions are generally synthesised through one or more variables, often referred to as indicators. When indicators are obtained through an estimation process, it is crucial to understand if and how their estimation error – for example, sampling error – affects the resulting composite index.This paper presents a methodology based on a parametric bootstrap technique that evaluates to what extent uncertainty in indicators affects the reliability of the aggregate composite index. The method is applied to four composite indexes measuring the environmental performances of Italian regions based on real population and survey data.To our knowledge, this is the first attempt to measure the impact of indicators’ sampling error on composite indexes. If adequately generalised, our methodology could be used in the presence of measurement errors, non-response issues, or other kinds of non-sampling errors. 62-08 62P12 62P25 Ecology Caterina Giusti verfasserin aut Stefano Marchetti verfasserin aut Monica Pratesi verfasserin aut In Ecological Indicators Elsevier, 2021 127(2021), Seite 107740- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:127 year:2021 pages:107740- https://doi.org/10.1016/j.ecolind.2021.107740 kostenfrei https://doaj.org/article/ca84ceda1d7345619d1dbf7731769e7c kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X21004052 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 127 2021 107740-
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title	Does uncertainty in single indicators affect the reliability of composite indexes? An application to the measurement of environmental performances of Italian regions
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title_sort	does uncertainty in single indicators affect the reliability of composite indexes? an application to the measurement of environmental performances of italian regions
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title_auth	Does uncertainty in single indicators affect the reliability of composite indexes? An application to the measurement of environmental performances of Italian regions
abstract	In recent decades, the measurement and evaluation of important social and natural phenomena has significantly evolved, with many traditional measurements based on single variables increasingly being replaced by multidimensional approaches. One key aspect of these approaches is the development of composite indexes, usually real-value functions of multiple achievements of a group of units. The achievements in each of the selected dimensions are generally synthesised through one or more variables, often referred to as indicators. When indicators are obtained through an estimation process, it is crucial to understand if and how their estimation error – for example, sampling error – affects the resulting composite index.This paper presents a methodology based on a parametric bootstrap technique that evaluates to what extent uncertainty in indicators affects the reliability of the aggregate composite index. The method is applied to four composite indexes measuring the environmental performances of Italian regions based on real population and survey data.To our knowledge, this is the first attempt to measure the impact of indicators’ sampling error on composite indexes. If adequately generalised, our methodology could be used in the presence of measurement errors, non-response issues, or other kinds of non-sampling errors.
abstractGer	In recent decades, the measurement and evaluation of important social and natural phenomena has significantly evolved, with many traditional measurements based on single variables increasingly being replaced by multidimensional approaches. One key aspect of these approaches is the development of composite indexes, usually real-value functions of multiple achievements of a group of units. The achievements in each of the selected dimensions are generally synthesised through one or more variables, often referred to as indicators. When indicators are obtained through an estimation process, it is crucial to understand if and how their estimation error – for example, sampling error – affects the resulting composite index.This paper presents a methodology based on a parametric bootstrap technique that evaluates to what extent uncertainty in indicators affects the reliability of the aggregate composite index. The method is applied to four composite indexes measuring the environmental performances of Italian regions based on real population and survey data.To our knowledge, this is the first attempt to measure the impact of indicators’ sampling error on composite indexes. If adequately generalised, our methodology could be used in the presence of measurement errors, non-response issues, or other kinds of non-sampling errors.
abstract_unstemmed	In recent decades, the measurement and evaluation of important social and natural phenomena has significantly evolved, with many traditional measurements based on single variables increasingly being replaced by multidimensional approaches. One key aspect of these approaches is the development of composite indexes, usually real-value functions of multiple achievements of a group of units. The achievements in each of the selected dimensions are generally synthesised through one or more variables, often referred to as indicators. When indicators are obtained through an estimation process, it is crucial to understand if and how their estimation error – for example, sampling error – affects the resulting composite index.This paper presents a methodology based on a parametric bootstrap technique that evaluates to what extent uncertainty in indicators affects the reliability of the aggregate composite index. The method is applied to four composite indexes measuring the environmental performances of Italian regions based on real population and survey data.To our knowledge, this is the first attempt to measure the impact of indicators’ sampling error on composite indexes. If adequately generalised, our methodology could be used in the presence of measurement errors, non-response issues, or other kinds of non-sampling errors.
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title_short	Does uncertainty in single indicators affect the reliability of composite indexes? An application to the measurement of environmental performances of Italian regions
url	https://doi.org/10.1016/j.ecolind.2021.107740 https://doaj.org/article/ca84ceda1d7345619d1dbf7731769e7c http://www.sciencedirect.com/science/article/pii/S1470160X21004052 https://doaj.org/toc/1470-160X
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up_date	2024-07-03T19:37:49.071Z
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One key aspect of these approaches is the development of composite indexes, usually real-value functions of multiple achievements of a group of units. The achievements in each of the selected dimensions are generally synthesised through one or more variables, often referred to as indicators. When indicators are obtained through an estimation process, it is crucial to understand if and how their estimation error – for example, sampling error – affects the resulting composite index.This paper presents a methodology based on a parametric bootstrap technique that evaluates to what extent uncertainty in indicators affects the reliability of the aggregate composite index. The method is applied to four composite indexes measuring the environmental performances of Italian regions based on real population and survey data.To our knowledge, this is the first attempt to measure the impact of indicators’ sampling error on composite indexes. 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Does uncertainty in single indicators affect the reliability of composite indexes? An application to the measurement of environmental performances of Italian regions

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