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.
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Notes
- 1.
In specific cases, some variables can be directly measured (e.g., some objective information). In this case, variable and indicator coincide.
- 2.
In data analysis, indicators/items are technically defined “variables”; consequently, these are conceptually different from “latent variables.”
- 3.
By using multiple measures, random errors tend to compensate each other. Consequently, the measurement turns out to be more accurate. The greater the error component in one single measure, the larger the number of required measures needs to be.
- 4.
As pointed out, the proposed model is conceptually related to latent structural models that find analytic solutions through the application of the structural equations method (Asher 1983; Bartholomew and Knott 1999; Blalock 1964, 1974; Bohrnstedt and Knoke 1994; Lazarsfeld and Henry 1968; Long 1993a, 1993b; Maggino 2005a; Netemeyer et al. 2003; Saris and Stronkhorst 1990; Sullivan and Feldman 1981; Werts et al. 1974).
- 5.
Another nonalternative classification distinguishes them with reference to their polarity, positive or negative quality of life observations (see the contribution to this by Alex Michalos in Sirgy et al. 2006).
- 6.
Aggregation of scores collected at micro levels is a well-known issue in many scientific fields, like economics and informatics, where particular analytic approaches are applied (e.g., probabilistic aggregation analysis). In econometric fields, particular empirical methodologies have been developed, allowing the explanation of systematic individual differences (compositional heterogeneity) that can have important consequences in interpreting aggregated values (Stoker 1993).
Other attempts aimed at weighting average values by different criteria can be identified (Kalmijn and Veenhoven 2005; Veenhoven 2005).
- 7.
Identification of typologies requires particular analytic approaches, allowing homogeneous groups among individual cases to be identified (Aldenderfer and Blashfield 1984; Bailey 1994; Corter 1996; Hair et al. 1998; Lis and Sambin 1977):
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– Segmentation analysis, which can be conducted through different procedures (Hierarchical Cluster Analysis, Q Analysis)
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– Partitioning analysis, which can be conducted through different procedures, like K Means Methods, Iterative Reclassification Methods, “Sift and Shift” Methods, Convergent MethodsEach analytic approach produces results that vary according to the decisions made in terms of (1) selected indicators, (2) measures used in order to evaluate proximities between individual-points, (3) method used in order to assign individual-points to a group, (4) criterion used in order to determine the number of groups, and (5) criterion used in order to check the interpretability of the groups.
-
- 8.
Equal weighting does not necessarily imply unitary weighting.
- 9.
- 10.
- 11.
The standard choice is for log as the concave down function and power as the concave up function.
- 12.
Anand and Sen (1997) state that, in measures of poverty deprivation “the relative impact of the deprivation … would increase as the level of deprivation becomes sharper”. According to this motivation, the UNDP develops measures of deprivation and inequality that more heavily penalize countries with higher indicators of deprivation in absolute value terms. For example, a decrease of 5 years of life expectancy from a base level of 40 is more heavily penalized than the same decrease beginning at a level of 80 (Sharpe and Salzman 2004).
- 13.
The possibility of applying techniques such as cluster analysis should not be ignored since these techniques allow different and alternative typologies to be evaluated among the observed cases.
- 14.
Receiver operating characteristic or relative operating characteristic analysis represents a valid method to be applied in order to test the discriminant capacity of a composite indicator. This analysis, connected directly to cost/benefit analysis in the area of diagnostic decision making, allows the relationship between sensitivity and specificity to be studied and analyzed in order to identify discriminant cut-point, cut-off, or operating-point.
ROC analysis is realized by studying the function that relates:
-
– The probability of obtaining a “true alarm” among cases that needs an action (→ sensitivity → hit rate → HR).
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– The probability of obtaining a “false alarm” among cases that do not need an action (→ 1-specificity → false alarm rate → FAR).
In order to study this relationship, two rates are computed for each cut-point. An optimal curve can be obtained by defining many cut-points along the supposed continuum of the composite indicator.
The procedure was conceived during the Second World War in order to study and improve the reception of radars and sonars. (Peterson, W. W., Birdsall, T. G., & Fox, W. C. (1954). The theory of signal detectability. Institute of Radio Engineers Transactions, PGIT-4, 171–212.).
-
- 15.
Multilevel analysis has been extended to include multilevel structural equation modeling, multilevel latent class modeling, and other more general models.
- 16.
Bayesian networks are based upon the concept of conditional probability. Conditional probability is the probability of some event A, given the occurrence of some other event B. Conditional probability is written P(A|B), and is read “the probability of A, given B.” The conditional and marginal probabilities of two random events are related in probability theory by Bayes’ theorem (often called Bayes’ law after Rev Thomas Bayes). It is often used to compute posterior probabilities given observations. For example, a patient may be observed to have certain symptoms. Bayes’ theorem can be used to compute the probability that a proposed diagnosis is correct, given that observation.
As a formal theorem, Bayes’ theorem is valid in all common interpretations of probability. However, it plays a central role in the debate around the foundations of statistics: frequentist and Bayesian interpretations disagree about the ways in which probabilities should be assigned in applications. According to the frequentist approach, probabilities are assigned to random events according to their frequencies of occurrence or to subsets of populations as proportions of the whole. In the Bayesian perspective, probabilities are described in terms of beliefs and degrees of uncertainty.
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Maggino, F., Zumbo, B.D. (2012). Measuring the Quality of Life and the Construction of Social Indicators. In: Land, K., Michalos, A., Sirgy, M. (eds) Handbook of Social Indicators and Quality of Life Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2421-1_10
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