Abstract
Many phenomena are complex and therefore difficult to measure and to evaluate. Research, in the last years, has been focusing on the development and use of a system of Composite Indicators in order to obtain a global description of a complex phenomenon and to convey a suitable synthesis of information. The existing literature offers several alternative methods for obtaining a Composite Indicators. The work focuses on building them through to Structural Equation Modeling, specifically with the use of Partial Least Squares-Path Modeling. In recent years many advances have been developed, in the context of these models to solve some problems related to the role that the Composite Indicators play within that system; in particular, the research focuses on a particular aspect linked to the high level of abstraction, when a Composite Indicator is manifold, lacks its own manifest variables and is described by various underlying blocks. In this regard we have proposed two alternative methods for analyzing and studying higher-order construct Composite Indicator, on the calculation of the estimates for the determination of endogenous block, so as to be the best estimated and represented by the blocks below.
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References
Bernard, P.: La cohésion sociale: critique d’un quasi-concept. Lien Soc. Politiques RIAC 41, 47–59 (1999)
Bollen, K.A.: Structural Equations with Latent Variables. Wiley, New York (1989)
Cataldo, R.: Developments in PLS-PM for the building of a system of Composite Indicators. Ph.D. Thesis, University of Naples Federico II (2016)
Cherchye, L., Moesen, W., Van Puyenbroeck, T.: Legitimately diverse. Yet comparable: on synthesising social inclusion performance in the EU. J. Common Mark. Stud. 42, 919–955 (2004)
Chin, W.W.: Issues and opinion on structural equation modeling. Manag. Inf. Syst. Q 22(1), 7–16 (1998)
Chin, W.W.: The partial least squares approach to structural equation modeling. In: Marcoulides, G.A. (ed.) Modern Business Research Methods, pp. 295–336. Lawrence Erlbaum Associates, Mahwah (1998)
De Muro, P., Mazziotta, M., Pareto, A.: Composite indices of development and poverty: an application to MDG Indicators. Soc. Indic. Res. 104(1), 1–18 (2011)
Funtowicz, S.O., Ravetz, J.R.: Uncertainty and Quality in Science for Policy. Kluwer Academic Publishers, Dordrecht (1996)
Grassia, M.G., Lauro, N.C., Marino, M., Pandolfo, M.: Indicatori Compositi da modello per lo studio della povertá e l’esclusione sociale. Espanet Italia (2014)
Henseler, J., Sarstedt, M.: Goodness-of-fit indices for partial least squares path modeling. Comput. Stat. 28(2), 565–580 (2013)
Hock, C., Ringle, C.M., Sarstedt, M.: Management of multipurpose stadiums: importance and performance measurement of service interfaces. Int. J. Serv. Technol. Manag. 14, 188–207 (2010)
Jarvis, D., MacKenzie, S., Podsakoff, P.: A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Consum. Res. 30(3), 199–218 (2003)
Jenson, J.: Mapping social cohesion: the state of canadian research. CPRN Stud, F/03 (1998)
Joseph, F., Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, Inc, Thousand Oaks (2014)
Kaplan, D.: Structural Equation Modeling: Foundations and Extensions. Sage Publications Inc., Thousands Oaks (2000)
Kettaneha, N., Berglundb, A., Wold, S.: PCA and PLS with very large data sets. Comput. Stat. Data Anal. 48(1), 69–85 (2005)
Lohmöller, J.B.: Latent Variable Path Modeling with Partial Least Squares. Springer, New york (1989)
Mahlberg, B., Obersteiner, M.: Remeasuring the HDI by Data Envelopment Analysis. International Institute for Applied System Analysis, Laxenburg (2001)
Mazziotta, M., Pareto, A.: Nuove misure del benessere: dal quadro teorico alla sintesi degli indicatori. SISmagazine, rivista on-line della Societá Italiana di Statistica (2011)
Nardo, M., Saisana, M., Saltelli, A., Tarantola, S.: Tools for Composite Indicators Building. European Commission, Brussels (2005)
Paxton, P., Curran, P.J., Bollen, K.A., Kirby, J., Chen, F.: Monte carlo experiments: design and implementation. Multidiscip. J. 8(2), 287–312 (2001)
Rajala, R., Westerlund, M.: Antecedents to consumers’ acceptance of mobile advertisements: a hierarchical construct PLS structural equation model. \(XLIII^{th}\) Hawaii International Conference on Systems Sciences (HICSS) (2010)
Reig-Martinez, E.: Social and economic Wellbeing in Europe and the Mediterranean Basin: building an enlarged human development indicator. Soc. Indic. Res. 111(2), 527–547 (2013)
Reinartz, W., Echambadi, R., Chin, W.W.: Generating non-normal data for simulation of structural equation models using Mattson’s method. Multivar. Behav. Res. 37(2), 227–244 (2002)
Saisana M., Tarantola S.: State-of-the-art Report on Current Methodologies and Practices for Composite Indicator Development. Institute for the Protection and Security of the Citizen Econometrics and Statistical Support to Antifraud Unit (2002)
Salzman, J.: Methodological Choices Encountered in the Construction of Composite Indices of Economic and Social Well-Being. Center for the Study of Living Standards, Ottawa (2003)
Sanchez, G.: PLS Path Modeling with R. Trowchez Editions, Berkeley (2010)
Sen, A.: Poverty and Famines: An Essay on Entitlement and Deprivation. Clarendon Press, Oxford (1981)
Tenenhaus, M., Amato, S., Esposito, Vinzi, V.: A global goodness-of-fit index for PLS structural equation modelling. In: Proceedings of \(XLII^{th}\) SIS Scientific Meeting (2004)
Tenenhaus, M., Vinzi, E.V., Chatelin, Y.M., Lauro, N.C.: PLS path modeling. Comput. Stat. Data Anal. 48(1), 159–205 (2005)
United Nations Development Progamme: Human Development Report, New York (2010)
Venaik, S.: A Model of Global Marketing in Multinational Firms: An Empirical Investigation. Unpublished Doctoral Dissertation. The Australian Graduate School of Management, Sydney (1999)
Vinzi, E.V., Trinchera, L., Amato, S.: PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. In: Vinzi, E.V., Chin, W.W., Henseler, J., Wang, H. (eds.) Handbook of Partial Least Squares (PLS): Concepts, Methods and Applications. Springer, Berlin (2010)
von Bertalanffy, L.: General System Theory: Foundations, Development, Applications. George Braziller, New York (1968)
Wilson, B.: Using PLS to investigate interaction effects between higher order brand constructs. In: Vinzi, E.V., Chin, W.W., Henseler, J., Wang, H. (eds.) Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing and Related Fields. Springer, New York (2009)
Wold, H.: PLS path models with latent variables: the nipals approach. In: Blalock, H.M., Aganbegian, A., Borodkin, F.R., Boudon, R., Cappecchi, V. (eds.) Quantitative Sociology: International Perspectives on Mathematical and Statistical Modeling. Academic Press, New York (1975)
Wold, H.: Soft modeling: the basic design and some extensions. In: Jöreskog, K.G., Wold, H. (eds.) Systems under Indirect Observation: Causality, Structure, Prediction, Part 2, pp. 1–54. Amsterdam, North-Holland (1982)
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Cataldo, R., Grassia, M.G., Lauro, N.C. et al. Developments in Higher-Order PLS-PM for the building of a system of Composite Indicators. Qual Quant 51, 657–674 (2017). https://doi.org/10.1007/s11135-016-0431-1
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DOI: https://doi.org/10.1007/s11135-016-0431-1