262
L. S. Alaimo and P. Conigliaro
Carlsen, L., & Bruggemann, R. (2017). Fragile state index: Trends and developments. A partial
order data analysis. Social Indicators Research, 133(1), 1–14. https://doi.org/10.1007/s11205016-1353-y
Clark, A. E., & Senik, C. (2011). Is happiness different from flourishing? Cross-country evidence
from the ESS. PSE Working Papers n.2011–04. https://doi.org/10.3917/redp.211.0017
Conigliaro, P. (2018). Labour status and subjective well-being. A micro-level analysis and a
multidimensional approach to well-being. Working papers of the Ph.D. Course in Applied
Social Sciences, Department of Social Sciences and Economics, Sapienza University of Rome.
WP n. 4/2018. https://web.uniroma1.it/disse/sites/default/files/WP4_Conigliaro_0.pdf
Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macro-theory of human
motivation, development and health. Canadian Psychology, 49(3), 182–185. https://doi.org/
10.1037/a0012801
Diener, E., & Emmons, R. A. (1984). The Independence of positive and negative affects.
Journal of Personality and Social Psychology, 47(5), 1105–1117. https://doi.org/10.1037//
0022-3514.47.5.1105
Diener, E., Wirtz, D., Tov, W., Kim-Prieto, C., Choi, D., Oishi, S., & Biswas-Diener, R. (2010).
New Well-being measures: Short scales to assess flourishing and positive and negative feelings.
Social Indicators Research, 97, 143–156. https://doi.org/10.1007/s11205-009-9493-y
Diener, E., Oishi, S., & Tay, L. (2018). Advances in subjective Well-being research. Nature Human
Behaviour, 2, 253–260. https://doi.org/10.1038/s41562-018-0307-6
Eurostat. (2013). Eu-Silc module on wellbeing – Assessment of the implementation. https://
ec.europa.eu/eurostat/documents/1012329/1012401/2013+Module+assessment.pdf.
Eurostat. (2015). Quality of life, fact and views. Luxembourg: Publications Office of the European
Union. https://doi.org/10.2785/59737
Eurostat. (2016). Analytical report on subjective Well-being. Luxembourg: Publications Office of
the European Union. https://doi.org/10.2785/318297
Fattore, M. (2016). Partially ordered sets and the measurement of multidimensional ordinal
deprivation. Social Indicators Research, 128, 835–858. https://dx.doi.org/10.1007/s11205-0151059-6
Fattore, M. (2017). Synthesis of indicators: The non-aggregative approach. In F. Maggino (Ed.),
Complexity in society: From indicators construction to their synthesis (pp. 192–212). Cham:
Springer. https://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-3-319-60595-1_8
Fattore, M. (2018). Non-aggregated indicators of environmental sustainability. Silesian Statistical
Review/Slaski Przeglad Statystyczny, 16(22), 7–22. https://doi.org/10.15611/sps.2018.16.01
Fattore, M., & Bruggemann, R. (2017). Partial order concepts in applied sciences. Cham: Springer.
Internet resources. https://doi.org/10.1007/978-3-319-45421-4
Fattore, M., Bruggemann, R., & Owsi´ nski, J. (2011). Using poset theory to compare fuzzy multidimensional material deprivation across regions. In S. Ingrassia, R. Rocci, & M. Vichi (Eds.),
New perspectives in statistical modeling and data analysis (pp. 49–56). Berlin/Heidelberg:
Springer. https://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-3-642-11363-5_6
Fattore, M., Maggino, F., & Colombo, E. (2012). From composite indicators to partial orders:
Evaluating socio-economic phenomena through ordinal data. In F. Maggino & G. Nuvolati
(Eds.), Quality of life in Italy: Research and reflections (pp. 41–68). Dordrecht: Springer. https:/
/doi.org/10.1007/978-94-007-3898-0_4
Fattore, M., Maggino, F., & Arcagni, A. (2015). Exploiting ordinal data for subjective well-being
evaluation. Statistics in Transition: Journal of the Polish Statistical Association, 16, 409–428.
https://doi.org/10.21307/stattrans-2015-023
Gallie, D., Gosetti, G., & La Rosa, M. (2012). Qualità del Lavoro e della Vita Lavorativa: Cosa
èCambiato e Cosa sta Cambiando. Milano: FrancoAngeli.
Huppert, F. A., & So, T. T. C. (2013). Flourishing across Europe: Application of a new conceptual
framework for defining Well-being. Social Indicators Research, 110(3), 837–861. https://
doi.org/10.1007/s11205-011-9966-7
L. S. Alaimo and P. Conigliaro
Carlsen, L., & Bruggemann, R. (2017). Fragile state index: Trends and developments. A partial
order data analysis. Social Indicators Research, 133(1), 1–14. https://doi.org/10.1007/s11205016-1353-y
Clark, A. E., & Senik, C. (2011). Is happiness different from flourishing? Cross-country evidence
from the ESS. PSE Working Papers n.2011–04. https://doi.org/10.3917/redp.211.0017
Conigliaro, P. (2018). Labour status and subjective well-being. A micro-level analysis and a
multidimensional approach to well-being. Working papers of the Ph.D. Course in Applied
Social Sciences, Department of Social Sciences and Economics, Sapienza University of Rome.
WP n. 4/2018. https://web.uniroma1.it/disse/sites/default/files/WP4_Conigliaro_0.pdf
Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macro-theory of human
motivation, development and health. Canadian Psychology, 49(3), 182–185. https://doi.org/
10.1037/a0012801
Diener, E., & Emmons, R. A. (1984). The Independence of positive and negative affects.
Journal of Personality and Social Psychology, 47(5), 1105–1117. https://doi.org/10.1037//
0022-3514.47.5.1105
Diener, E., Wirtz, D., Tov, W., Kim-Prieto, C., Choi, D., Oishi, S., & Biswas-Diener, R. (2010).
New Well-being measures: Short scales to assess flourishing and positive and negative feelings.
Social Indicators Research, 97, 143–156. https://doi.org/10.1007/s11205-009-9493-y
Diener, E., Oishi, S., & Tay, L. (2018). Advances in subjective Well-being research. Nature Human
Behaviour, 2, 253–260. https://doi.org/10.1038/s41562-018-0307-6
Eurostat. (2013). Eu-Silc module on wellbeing – Assessment of the implementation. https://
ec.europa.eu/eurostat/documents/1012329/1012401/2013+Module+assessment.pdf.
Eurostat. (2015). Quality of life, fact and views. Luxembourg: Publications Office of the European
Union. https://doi.org/10.2785/59737
Eurostat. (2016). Analytical report on subjective Well-being. Luxembourg: Publications Office of
the European Union. https://doi.org/10.2785/318297
Fattore, M. (2016). Partially ordered sets and the measurement of multidimensional ordinal
deprivation. Social Indicators Research, 128, 835–858. https://dx.doi.org/10.1007/s11205-0151059-6
Fattore, M. (2017). Synthesis of indicators: The non-aggregative approach. In F. Maggino (Ed.),
Complexity in society: From indicators construction to their synthesis (pp. 192–212). Cham:
Springer. https://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-3-319-60595-1_8
Fattore, M. (2018). Non-aggregated indicators of environmental sustainability. Silesian Statistical
Review/Slaski Przeglad Statystyczny, 16(22), 7–22. https://doi.org/10.15611/sps.2018.16.01
Fattore, M., & Bruggemann, R. (2017). Partial order concepts in applied sciences. Cham: Springer.
Internet resources. https://doi.org/10.1007/978-3-319-45421-4
Fattore, M., Bruggemann, R., & Owsi´ nski, J. (2011). Using poset theory to compare fuzzy multidimensional material deprivation across regions. In S. Ingrassia, R. Rocci, & M. Vichi (Eds.),
New perspectives in statistical modeling and data analysis (pp. 49–56). Berlin/Heidelberg:
Springer. https://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-3-642-11363-5_6
Fattore, M., Maggino, F., & Colombo, E. (2012). From composite indicators to partial orders:
Evaluating socio-economic phenomena through ordinal data. In F. Maggino & G. Nuvolati
(Eds.), Quality of life in Italy: Research and reflections (pp. 41–68). Dordrecht: Springer. https:/
/doi.org/10.1007/978-94-007-3898-0_4
Fattore, M., Maggino, F., & Arcagni, A. (2015). Exploiting ordinal data for subjective well-being
evaluation. Statistics in Transition: Journal of the Polish Statistical Association, 16, 409–428.
https://doi.org/10.21307/stattrans-2015-023
Gallie, D., Gosetti, G., & La Rosa, M. (2012). Qualità del Lavoro e della Vita Lavorativa: Cosa
èCambiato e Cosa sta Cambiando. Milano: FrancoAngeli.
Huppert, F. A., & So, T. T. C. (2013). Flourishing across Europe: Application of a new conceptual
framework for defining Well-being. Social Indicators Research, 110(3), 837–861. https://
doi.org/10.1007/s11205-011-9966-7