Principal component analysis applied to multidimensional social indicators longitudinal studies: limitations and possibilities

Libório, Matheus Pereira; Martinuci, Oséias da Silva; Machado, Alexei Manso Corrêa; Machado-Coelho, Thiago Melo; Laudares, Sandro; Bernardes, Patrícia

doi:10.1007/s10708-020-10322-0

Cited by 18 publications

(16 citation statements)

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“…However, Libório et al, (2020aLibório et al, ( , 2020b show that the normalization of indicators based on single data sets distorts the scores of the composite indicator insofar as the values of maximum, minimum, standard deviation, or average used in this normalization are different from the respective values calculated for each time separately. Even so, it is possible to affirm that any method of constructing composite indicators can be employed in the time-space analysis, but that such methods are associated with uncertainties and challenges in the definition of cut-off levels (Alkire & Foster, 2011) and in the normalization of indicators (Nardo et al, 2005;Diaga & Giang 2017;Cinelli et al, 2021).…”

Section: What Methods Can Be Employed?mentioning

confidence: 99%

“…In common, absolute and relative time-space analyses are associated with the support change problem (Libório et al, 2020a(Libório et al, , 2020b. The support change problem occurs when the space of origin of the demographic census data, that is, the census tracts, changes over time (Umbelino & Barbieri, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…In turn, the choice of the normalization technique is subjective and brings uncertainty to the composite indicator (Dialga and Giang 2017;Cinelli 2021). Besides, the value used in normalization is different in time, which results in distinct composite indicators (Landi et al, 2018;Libório et al, 2020aLibório et al, , 2020b. Therefore, it may not be plausible to state that the normalization of the indicators through z-scores allows for comparing observations over time (Nilsson & Delmelle, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

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Composite Indicators are one-dimensional measurements that simplify the interpretation of multidimensional phenomena that facilitate public policies' elaboration. The literature on composite indicators is abundant, diversified, and inserted in practically all knowledge areas. Part of this literature aims to reduce uncertainties that propagate through the structure of the composite indicator during the process of normalization, weighting, and aggregation of indicators. Even if no composite indicator is exempt from criticism, the current literature is already sufficiently large and deep to guide researchers in constructing reliable composite indicators. However, most related works are concerned with representing multidimensional phenomena in time or space. Although some studies are interested in representing multidimensional phenomena that co-occur in time-space, the portion of the literature that addresses composite indicators is still not comprehensive, therefore leaving several open questions: What are the additional challenges in representing multidimensional phenomena in time-space? What methods can be used? Which method is most appropriate for this type of representation? What are the shortcomings of this method? How to reduce these shortcomings? This research aims at answering these questions in order to advance the time-space analysis of multidimensional phenomena. As a general contribution, the work presents a scheme of procedures that reduce subjectivities and uncertainties in the representations of multidimensional phenomena in time-space. As a specific contribution, it provides accurate and reliable information on the trajectory of social exclusion in the analyzed region. KeywordsComposite indicators • Multidimensional phenomena • Cluster analysis • Principal component analysis • Time-in-space analysis • Urban geography

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Section: What Methods Can Be Employed?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

et al. 2021

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“…Composite indicators are one-dimensional measures of complex phenomena that typically involve many sub-indicators (Otoiu et al, 2021 ). This property is advantageous in geography because it allows representing many sub-indicators in a single map (Libório et al, 2020 ). The advantages of representing complex phenomena simply through composite indicators have conquered researchers from the most different areas of knowledge (Greco et al, 2019 ).…”

Section: Operational Frameworkmentioning

confidence: 99%

“…In stage one, the CI-Exposure-PCA is constructed using the Software R. In stage two, the Variance Extracted (VE) in the Principal Component and the Kaiser–Meyer–Olkin (KMO, Kaiser, 1974 ) test are checked. The CI-Exposure-PCA is considered statistically consistent when the VE and the KMO exceed 0.50 and 0.60, respectively (Libório et al, 2020 ).…”

Section: Operational Frameworkmentioning

confidence: 99%

Factors that most expose countries to COVID-19: a composite indicators-based approach

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Studies carried out in different countries correlate social, economic, environmental, and health factors with the number of cases and deaths from COVID-19. However, such studies do not reveal which factors make one country more exposed to COVID-19 than other. Based on the composite indicators approach, this research identifies the factors that most impact the number of cases and deaths of COVID-19 worldwide and measures countries’ exposure to COVID-19. Three composite indicators of exposure to COVID-19 were constructed through Principal Component Analysis, Simple Additive Weighting, and k-means clustering. The number of cases and deaths from COVID-19 is strongly correlated ( R > 0.60) with composite indicator scores and moderately concordant ( K > 0.4) with country clusters. Factors directly or indirectly associated with the age of the population are the ones that most expose countries to COVID-19. The population of countries most exposed to COVID-19 is 12 years older on average. The proportion of the elderly population in these countries is at least twice that of countries less exposed to COVID-19. Factors that can increase the population’s life expectancy, such as Gross Domestic Product per capita and the Human Development Index, are four times and 1.3 times higher in more exposed countries to COVID-19. Providing better living conditions increases both the population’s life expectancy and the country’s exposure to COVID-19.

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Measuring inequality through a non-compensatory approach

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Principal component analysis applied to multidimensional social indicators longitudinal studies: limitations and possibilities

Cited by 18 publications

References 50 publications

Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

Factors that most expose countries to COVID-19: a composite indicators-based approach

Measuring inequality through a non-compensatory approach

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