<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The average expected duration of human life is rising because of different reasons. On the other hand, not only the duration, but the quality of life level is important, too. The higher the quality of life level, the citizens’ happiness and satisfaction levels are higher, which has positive impact on the development and operating of an economy. The goal of this paper is to identify groups of European countries, using statistical hierarchical cluster analysis, by using the quality of life indicators, and to recognise differences in quality of life levels. The quality of life is measured by using seven different indicators. The conducted statistical hierarchical cluster analysis is based on the Ward’s clustering method, and squared Euclidean distances. The results of conducted statistical hierarchical cluster analysis enabled recognizing of three different groups of European countries: old European Union member states, new European Union members, and non-European Union member states. The analysis has revealed that the old European Union member states seem to have in average higher quality of life level than the new European Union member states. Furthermore, the European Union member states have in average higher quality of live level than non-European Union members do. The results indicate that quality of life levels and economic development levels are connected.</p></abstract>

The average expected duration of human life is rising because of different reasons. On the other hand, not only the duration, but the quality of life level is important, too. The higher the quality of life level, the citizens’ happiness and satisfaction levels are higher, which has positive impact on the development and operating of an economy. The goal of this paper is to identify groups of European countries, using statistical hierarchical cluster analysis, by using the quality of life indicators, and to recognise differences in quality of life levels. The quality of life is measured by using seven different indicators. The conducted statistical hierarchical cluster analysis is based on the Ward’s clustering method, and squared Euclidean distances. The results of conducted statistical hierarchical cluster analysis enabled recognizing of three different groups of European countries: old European Union member states, new European Union members, and non-European Union member states. The analysis has revealed that the old European Union member states seem to have in average higher quality of life level than the new European Union member states. Furthermore, the European Union member states have in average higher quality of live level than non-European Union members do. The results indicate that quality of life levels and economic development levels are connected.

Quality of Life Indicators in Selected European Countries: Hierarchical Cluster Analysis Approach

Faculty of Economics and Business, University of Zagreb, Zagreb,

Croatian Review of Economic, Business and Social Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The average expected duration of human life is rising because of different reasons. On the other hand, not only the duration, but the quality of life level is...

Quality of Life Indicators in Selected European Countries: Hierarchical Cluster Analysis Approach

Online veröffentlicht: 17. Okt. 2016

Seitenbereich: 42 - 54

Eingereicht: 01. Okt. 2015

Akzeptiert: 01. Dez. 2015

DOI: https://doi.org/10.1515/crebss-2016-0004

Schlüsselwörter
quality of life indicators, Ward’s method, outlier detection, European countries, analysis of variance (ANOVA)

© 2015 Berislav Žmuk, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Quality of Life Indicators in Selected European Countries: Hierarchical Cluster Analysis Approach

Online veröffentlicht: 17. Okt. 2016

Seitenbereich: 42 - 54

Eingereicht: 01. Okt. 2015

Akzeptiert: 01. Dez. 2015

DOI: https://doi.org/10.1515/crebss-2016-0004

Schlüsselwörterquality of life indicators, Ward’s method, outlier detection, European countries, analysis of variance (ANOVA)

© 2015 Berislav Žmuk, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
quality of life indicators, Ward’s method, outlier detection, European countries, analysis of variance (ANOVA)