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Model Based Composite Indicators: New Developments in Partial Least Squares-Path Modeling for the Building of Different Types of Composite Indicators

Author

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  • Natale Carlo Lauro

    (University “Federico II”)

  • Maria Gabriella Grassia

    (University “Federico II”)

  • Rosanna Cataldo

    (University “Federico II”)

Abstract

Composite indicators (CIs), in the social sciences, are used more and more for measuring very complex phenomena as the poverty, the progress and the well-being. Using an approach Model Based in to build CIs, instead of an approach Data Driven, it is possible to consider the role (formative and reflective) of the manifest variables (MVs) and to model the relationships among the CIs. In this article, we begin introducing structural equation modeling (SEM) as a tool for building Model Based CIs. Secondly, among the several methods developed to estimate SEM, we show Partial Least Squares Path Modeling (PLS-PM), due to the key role that estimation of the latent variables (i.e. the CIs) plays in the estimation process. Moreover, we present some recent developments in PLS-PM for the treatment of non metric data, hierarchical data, longitudinal data and multi-block data. Finally, we demonstrate how these recent developments can strongly help in the building of CIs. It is easy to realize, for example, that as a consequence of considering nominal and ordinal data, the knowledge about a phenomenon synthesized by a CI is considerably extended and improved especially for operational use. In order to highlight the potentiality of the proposed approach, the construction of a CI is discussed. In particular, a CI of Social Cohesion, developed by using European Values Study data, will be described in detail.

Suggested Citation

  • Natale Carlo Lauro & Maria Gabriella Grassia & Rosanna Cataldo, 2018. "Model Based Composite Indicators: New Developments in Partial Least Squares-Path Modeling for the Building of Different Types of Composite Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 421-455, January.
    • Handle: RePEc:spr:soinre:v:135:y:2018:i:2:d:10.1007_s11205-016-1516-x
    DOI: 10.1007/s11205-016-1516-x
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    Cited by:

    1. Fattore, Marco & Alaimo, Leonardo Salvatore, 2023. "A partial order toolbox for building synthetic indicators of sustainability with ordinal data," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    2. Rosanna Cataldo & Corrado Crocetta & Maria Gabriella Grassia & Natale Carlo Lauro & Marina Marino & Viktoriya Voytsekhovska, 2021. "Methodological PLS-PM Framework for SDGs System," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 701-723, August.
    3. Carlo Cavicchia & Maurizio Vichi, 2021. "Statistical Model-Based Composite Indicators for Tracking Coherent Policy Conclusions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 449-479, August.
    4. Venera Tomaselli & Mario Fordellone & Maurizio Vichi, 2021. "Building Well-Being Composite Indicator for Micro-Territorial Areas Through PLS-SEM and K-Means Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 407-429, January.
    5. Yelin Fu & Kong Xiangtianrui & Hao Luo & Lean Yu, 2020. "Constructing Composite Indicators with Collective Choice and Interval-Valued TOPSIS: The Case of Value Measure," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 117-135, November.
    6. Corrado Crocetta & Laura Antonucci & Rosanna Cataldo & Roberto Galasso & Maria Gabriella Grassia & Carlo Natale Lauro & Marina Marino, 2021. "Higher-Order PLS-PM Approach for Different Types of Constructs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 154(2), pages 725-754, April.
    7. Tianjiao Wang & Yelin Fu, 2020. "Constructing Composite Indicators with Individual Judgements and Best–Worst Method: An Illustration of Value Measure," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(1), pages 1-14, May.
    8. Svenja Damberg & Lena Frömbling, 2022. "“Furry tales”: pet ownership’s influence on subjective well-being during Covid-19 times," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3645-3664, October.
    9. Matheus Pereira Libório & Oseias da Silva Martinuci & Sandro Laudares & Renata de Mello Lyrio & Alexei Manso Correa Machado & Patrícia Bernardes & Petr Ekel, 2020. "Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator," Sustainability, MDPI, vol. 12(20), pages 1-18, October.

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