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2021 | OriginalPaper | Buchkapitel

Construction of an Immigrant Integration Composite Indicator through the Partial Least Squares Structural Equation Model K-Means

verfasst von : Venera Tomaselli, Mario Fordellone, Maurizio Vichi

Erschienen in: Data Science and Social Research II

Verlag: Springer International Publishing

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Abstract

Integration is a multidimensional process, which can take place in different ways and at different times in relation to each of the single economic, social, cultural, and political dimensions. Hence, examining every single dimension is important as well as building composite indexes simultaneously inclusive of all dimensions in order to obtain a complete description of a complex phenomenon and to convey a coherent set of information. In this paper, we aim at building an immigrant integration composite indicator (IICI), able to measure the different aspects related to integration such as employment, education, social inclusion, active citizenship, and on the basis of which to simultaneously classify territorial areas such as European regions. For this application, the data collected in 274 European regions from the European Social Survey (ESS), Round 8, on immigration have been used.

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For more details on the selected MVs, you can see the official ESS website: http://www.europeansocialsurvey.org/about/news/essnews0038.html..

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Titel: Construction of an Immigrant Integration Composite Indicator through the Partial Least Squares Structural Equation Model K-Means
verfasst von: Venera Tomaselli
Mario Fordellone
Maurizio Vichi
Verlag: Springer International Publishing
Buch: Data Science and Social Research II
Print ISBN: 978-3-030-51221-7

Electronic ISBN: 978-3-030-51222-4

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-51222-4_28

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