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Exploring Multidimensional Well-Being in Switzerland: Comparing Three Synthesizing Approaches

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Katia Iglesias at HES-SO University of Applied Sciences and Arts Western Switzerland
Katia Iglesias
Suter Christian at University of Neuchâtel
Suter Christian
Tugce Beycan
Tugce Beycan
Tugce Beycan
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Vani Balasubramanian at Institute for Social and Economic Change
Vani Balasubramanian
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Abstract and Figures

Well-being is increasingly conceptualized and measured from a multidimensional perspective in order to provide policy makers and the general public with the necessary tools for monitoring social change. Synthetic measures of multidimensional concepts such as well-being are, therefore, needed. This paper provides a systematic comparison of three methodological approaches applicable in a top-down perspective to construct a synthetic multidimensional measure of well-being using the same theoretical framework and the same data. The three methodologies used are the confirmatory factor analysis, the Alkire and Foster counting approach and the posetic approach. The first two methodologies belong to the family of composite indicators, the most common way to synthetize multidimensional information. The third was developed in order to deal with the construction of composite indicators’ two major issues, namely weighting and aggregating. The main contribution of this paper is methodological; it aims at discussing the advantages and drawbacks of the three methodologies, and at identifying their similarities and specificities.
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Exploring Multidimensional Well-Being in Switzerland:
Comparing Three Synthesizing Approaches
Katia Iglesias
1
•Christian Suter
2
•Tugce Beycan
3
•
B. P. Vani
4
Accepted: 12 September 2016 / Published online: 17 September 2016
Springer Science+Business Media Dordrecht 2016
Abstract Well-being is increasingly conceptualized and measured from a multidimen-
sional perspective in order to provide policy makers and the general public with the
necessary tools for monitoring social change. Synthetic measures of multidimensional
concepts such as well-being are, therefore, needed. This paper provides a systematic
comparison of three methodological approaches applicable in a top-down perspective to
construct a synthetic multidimensional measure of well-being using the same theoretical
framework and the same data. The three methodologies used are the confirmatory factor
analysis, the Alkire and Foster counting approach and the posetic approach. The first two
methodologies belong to the family of composite indicators, the most common way to
synthetize multidimensional information. The third was developed in order to deal with the
construction of composite indicators’ two major issues, namely weighting and aggregating.
The main contribution of this paper is methodological; it aims at discussing the advantages
and drawbacks of the three methodologies, and at identifying their similarities and
specificities.
&Katia Iglesias
katia.iglesias@unine.ch
Christian Suter
christian.suter@unine.ch
Tugce Beycan
tugce.beycan@unine.ch
B. P. Vani
vani@isec.ac.in
1
Center for the Understanding of Social Processes, University of Neucha
ˆtel, A. L. Breguet 1,
2000 Neucha
ˆtel, Switzerland
2
Department of Sociology, University of Neucha
ˆtel, Fbg de l’Ho
ˆpital 27, 2000 Neucha
ˆtel,
Switzerland
3
Department of Sociology, Center for the Understanding of Social Processes, University of
Neucha
ˆtel, A. L. Breguet 1, 2000 Neucha
ˆtel, Switzerland
4
Institute for Social and Economic Change (ISEC), Nagarabhavi P.O., Bangalore 560072, India
123
Soc Indic Res (2017) 134:847–875
DOI 10.1007/s11205-016-1452-9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Only recently, poverty has been increasingly conceptualized and measured from a multidimensional perspective in order to provide policy makers and the general public with the necessary tools for effectively monitoring social changes (Iglesias et al. 2017). For instance, policy makers who have often underestimated the need to define poverty multidimensionally (Kana Zeumo et al. 2011), started to consider it as a multidimensional concept. ...
... Indeed, 15 articles (about 13%) have proposed latent variable models, such as factor analysis, or methods based on the concept of latent variables, such as principal component analysis or multiple correspondence analysis. In particular, one third of these studies use factor analysis, either in its confirmatory or exploratory version (see, e.g., Betti et al. 2015;Iglesias et al. 2017), one third use multiple correspondence analysis (see, e.g., Berenger et al. 2013), 4 of them use principal component analysis (see, e.g., Li et al. 2019) while, in the remaining study, latent class analysis based on discrete latent variables is proposed (Moonansingh et al. 2019). The aim of these studies has been to build a synthetic multidimensional measure of poverty or well-being, where each dimension is conceived as a latent, non-observable, construct. ...
... Moreover, the stochastic dominance and partial order theory have been proposed in 6 studies (about 5%) for synthetizing multidimensional data as opposed to the classical approaches based on composite indicators, such as the AF method or factor analysis. In particular, the posetic (partially ordered set) approach has been proposed in this field to deal with the issues of weighting and aggregating for ordinal data (Iglesias et al. 2017) by following the proposal of Fattore (2016) and Fattore et al. (2012). Interestingly, the studies by Fattore (2016) and Fattore et al. (2012) have not been included in the list of studies of our systematic review, despite they developed the initial proposal based on the posetic approach. ...
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