Measuring the Quality of Life and the Construction of Social Indicators

Chapter

Abstract

As is evident from even a cursory review of the research literature and current practices, the well-being of societies represents a multidimensional concept that is difficult and complex to define. Its quantitative measurement requires a multifaceted approach and a multipurpose methodology that is a mix of many approaches and techniques founded upon statistical indicators. The main notion that should be kept in mind in order to measure societal well-being from a quantitative perspective, using statistical indicators, is complexity. The complexity stems from the reality to be observed, and affects the measuring process and the construction of the indicators. Therefore, complexity should be preserved in analyzing indicators and should be correctly represented in telling stories from indicators. In considering the topics we wished to include in this chapter we chose to be inclusive with an eye toward integrating a vast body of methodological literature. Our aim in this chapter is to disentangle some important methodological approaches and issues that should be considered in measuring and analyzing quality of life from a quantitative perspective. Due to space limitations, relative to the breadth and scope of the task at hand, for some issues and techniques we will provide details whereas for others more general integrative remarks. The chapter is organized as follows. The first section (comprised of three subsections) deals with the conceptual definitions and issues in developing indicators. The aim of this first section, like the chapter as a whole, is to provide a framework and structure. The second section (comprised of three subsections) is an overview of the analytic tools and strategies. The third, and final, section (comprised of two subsections) focuses on methodological and institutional challenges.

Keywords

Latent Variable Data Envelopment Analysis Bayesian Network Composite Indicator Conceptual Definition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Netherlands 2012

Authors and Affiliations

  1. 1.Università degli Studi di FirenzeFlorenceItaly
  2. 2.University of British ColumbiaVancouverCanada

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