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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

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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.

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Notes

  1. The word systemic deriving from the definition of the system given by Von Bertalanffy (1983), according to which a system is a set of elements in interaction, not just an aggregation of EIs but a set of indicators linked to each other by mutual relationships, expressed through functional links and summarized in a specific model.

  2. LISREL is the acronym of Linear Structural Relationship a software created by Jöreskog in the seventies to estimate a covariance based SEM model.

  3. In this case the MVs should not covariate, because the latent concept is formed by the MVs.

  4. The convergence of the PLS-PM algorithm is demonstrated by Tenenhaus and Tenenhaus (2009).

  5. The first wave of the survey was launched in 1981 in ten European countries. To explore the dynamics of value changes, a second wave of surveys was launched in 1990 in all European countries, including Switzerland, Austria and countries in Central and Eastern Europe, as well as the US and Canada. About 10 years later (1999/2000), the third EVS survey was launched, the fieldwork being conducted in almost all European countries. The fourth wave was launched in 2008 (http://www.europeanvaluesstudy.eu).

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Authors and Affiliations

  1. Department of Economics and Statistics, University “Federico II”, Via Cintia - Monte Sant’Angelo, 80126, Naples, Italy

    Natale Carlo Lauro & Rosanna Cataldo

  2. Department of Social Science, University “Federico II”, Vico Monte della Pietá, 80138, Naples, Italy

    Maria Gabriella Grassia

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  1. Natale Carlo Lauro
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  2. Maria Gabriella Grassia
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  3. Rosanna Cataldo
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Correspondence to Rosanna Cataldo.

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Lauro, N.C., Grassia, M.G. & Cataldo, R. Model Based Composite Indicators: New Developments in Partial Least Squares-Path Modeling for the Building of Different Types of Composite Indicators. Soc Indic Res 135, 421–455 (2018). https://doi.org/10.1007/s11205-016-1516-x

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