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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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):
-
– Segmentation analysis, which can be conducted through different procedures (Hierarchical Cluster Analysis, Q Analysis)
-
– 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).
-
– 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.
References
Abbey, A., & Andrews, F. M. (1985). Modeling the psychological determinants of life quality. Social Indicators Research, 16, 1–34.
Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis (Sage university paper series on quantitative applications in the social sciences, series no. 07–044). Beverly Hills: Sage.
Alt, M. (1990). Exploring hyperspace. A non-mathematical explanation of multivariate analysis. New York/London: McGraw-Hill.
Anand, S., & Sen, A. (1997). Concepts of human development and poverty: A multidimensional perspective (Human development papers 1997). New York: UNDP.
Andersen, E. B. (1972). The numerical solution of a set of conditional estimation equations. Journal of the Royal Statistical Society, Series B, 34, 42–54.
Andersen, E. B. (1973). A goodness of fit test for the Rasch model. Psychometrika, 38, 123–140.
Anderson, T. W. (1958). An introduction to multivariate statistical analysis. New York/London/Sidney: Wiley.
Andrews, F. M., & Withey, S. B. (1976). Social indicators of well-being: Americans’ Perceptions of life quality. New York: Plenum Press.
Andrich, D. (1988). Rasch models for measurement (Sage university paper series on quantitative applications in the social sciences, series no. 07–068). Newbury Park: Sage.
Asher, H. B. (1983). Causal modelling (Sage university paper series on quantitative applications in the social sciences, series no. 07–003). Newbury Park: Sage.
Bamber, D. (1975) The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology, 12: 387–415.
Bailey, K. D. (1994). Typologies and taxonomies. An introduction to classification techniques (Sage university paper series on quantitative applications in the social sciences, series no. 07–102). Thousand Oaks: Sage.
Bartholomew, D. J., & Knott, M. (1999). Latent variable models and factor analysis, Kendall’s library of statistics (Vol. 7). London: Arnold Publishers.
Bottarelli, E., & Parodi, S. (2003). Un approccio per la valutazione della validità dei test diagnostici: le curve R.O.C. (Receiver Operating Characteristic)”. Ann. Fac. Medic. Vet. di Parma, XXIII: 49–68 (www.unipr.it/arpa/facvet/annali/2003/49.pdf)
Berger-Schmitt R., & Noll, H. -H. (2000). Conceptual framework and structure of a European system of social indicators (EuReporting working paper no. 9). Mannheim: Centre for Survey Research and Methodology (ZUMA) – Social Indicators Department
Bezzi, C., Cannavò, L., & Palumbo, M. (2009). Gli indicatori sociali e il loro uso valutativo. Milano: Franco Angeli Editore.
Blalock, H. M. (1964). Causal inferences in nonexperimental research. Chapel Hill: University of North Carolina Press.
Blalock, H. M. (Ed.). (1974). Measurement in the social sciences. Theories and strategies. Chicago: Aldine Publishing Company.
Bock, R. D., & Aitken, M. (1981). Marginal maximum likelihood estimation of item parameters: An application of an EM algorithm. Psychometrika, 46, 443–459.
Bohrnstedt, G. W., & Knoke, D. (1994). Statistics: A tool for social data analysis. Itasca: Peacock Publishers.
Bolasco, S. (1999). Analisi multidimensionali dei dati. Metodi, strategie e criteri di interpretazione. Roma: Carocci.
Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 10, 305–314.
Booysen, F. (2002). An overview and evaluation of composite indices of development. Social Indicators Research, 59, 115–151.
Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks: SAGE.
Campbell, A., Converse, P. E., & Rodgers, W. L. (1976). The quality of American life. New York: Russell Sage.
Cannavò, L. (2009). Dell’incertezza e della complessità: gli indicatori tra ricerca valutazione. In C. Bezzi, L. Cannavò, & M. Palumbo (Eds.), Gli indicatori sociali e il loro uso valutativo. Milano: Franco Angeli Editore.
Cooley, W. W., & Lohnes, P. R. (1971). Multivariate data analysis. New York/London/Sidney: Wiley.
Coombs, C. H., Dawes, R. M., and Tversky, A. (1970) Mathematical psychology: An elementary introduction. Englewood Cliffs: Prentice-Hall, Inc.
Corbetta, P. (1992). Metodi di analisi multivariata per le scienze sociali. Bologna: Il Mulino.
Corbetta, P. (2003). Metodologia e tecniche della ricerca sociale. Bologna: Il Mulino.
Corter, J. E. (1996). Tree models of similarity and association (Sage university paper series on quantitative applications in the social sciences, series no. 07–112). Thousand Oaks: Sage.
Costa, P. T., & McCrae, R. R. (1980). Still stable after all these years: personality as a key to some issues in adulthood and old age. In P. B. Baltes & O. G. Brim (Eds.), Life span development and behaviour (pp. 65–102). New York: Academic.
Costanza, R., Fisher, B., Ali, S., Beer, C., Bond, L., Boumans, R., et al. (2007). Quality of life: An approach integrating opportunities, human needs, and subjective well-being. Ecological Economics, 61, 267–276.
Cox, T. F., & Cox, M. A. A. (1994). Multidimensional scaling. London: Chapman & Hall.
Cummins, R. A. (1993). Comprehensive quality of life scale for adults (ComQol-A4). Melbourne: Deakin University, School of Psychology.
Cummins, R. A. (1995) On the trail of the gold standard for subjective well-being. Social Indicators Research, 35: 170–200.
De Vellis, R. (1991). Scale devolopment. Theory and applications (Applied social research methods series, Vol. 26). London: SAGE.
Diamantopoulos, A., & Siguaw, J. A. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17, 263–282.
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38, 269–277.
Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95, 542–575.
Diener, E., & Suh, E. (1997). Measuring quality of life: economic, social, and subjective indicators. Social Indicators Research, 40, 189–216.
Drenowski, J., & Wolf, S. (1966). The level of living index. Geneva: United Nations Research Institute for Social Development.
Drewnowski, J. (1970). A planning model for social development, Unrisd, (Report n.70.5), Geneva.
Easterlin, R. A. (1974). Does economics growth improve the human lot? Some empirical evidence. In P. A. David & M. W. Reder (Eds.), Nations and households in economic growth; essays in honor of Moses Abramowitz (pp. 89–125). New York: Academic.
Edward, W., & Newman, J. R. (1982). Multiattribute evalutation (Sage university paper series on quantitative applications in the social sciences, series no. 07–026). Newbury Park: Sage.
Egan, J. P. (1975) Signal detection theory and ROC analysis. New York: Academic Press.
Engel, U., & Reinecke, J. (1996). Analysis of change. Berlin/New York: Walter de Gruyter.
Epidemiological Bullettin. (2002). Introduction to social epidemiology. Epidemiological Bullettin, 23(1). http://www.paho.org/English/sha/be_v23n1-socialepi.htm
Erikson, R. (1993). Descriptions of inequality: The Swedish approach to welfare research. In M. Nussbaum & A. Sen (Eds.), The quality of life (pp. 67–83). Oxford: Oxford University Press.
Eurostat. (2000). Definition of quality in statistics and standard quality report, Eurostat.
Fattore, M., & Maggino, F. (2011). Socio-economic evaluation with ordinal variables: Integrating counting and poset approaches in Statistica & Applicazioni (forthcoming).
Fattore, M., Maggino, F., & Colombo, E. (2011). From composite indicators to partial orders: Evaluating socio-economic phenomena through ordinal data. In F. Maggino & G. Nuvolati (Eds.), Quality of Life: reflections, studies and researches in Italy. Social Indicators Research Series (forthcoming).
Firebaugh, G. (1997). Analyzing repeated surveys (Sage university paper series on quantitative applications in the social sciences, series no. 07–115). Newbury Park: Sage.
Freudenberg, M. (2003). Composite indicators of country performance: a critical assessment (STI working paper, 2003/16, Industry Issues). Paris: OECD. url: http://www.olis.oecd.org/olis/2003doc.nsf/43bb6130e5e86e5fc12569fa005d004c/8bb0f462911c2cc6c1256ddc00436279/$FILE/JT00153477.DOC
Frisch, M. B. (1998). Quality of life therapy and assessment in health care. Clinical Psychology: Science and Practice, 5, 19–40.
Ghiselli, E. E. (1964). Theory of psychological measurement. New York/London: McGraw-Hill.
Giovannini, E. (2007). The role of statistics in a globalised world: risks and challenges. Paper presented at the DGINS (Directors-General of the National Statistical Institutes) Conference, 20–21 September 2007, Budapest, Hungary
Glenn, N. D. (1977). Cohort analysis (Sage university paper series on quantitative applications in the social sciences, series no. 07–005). Newbury Park: Sage.
Global Water Partnership - Technical Committee. (2004). Monitoring and evaluation indicators for Integrated Water Resources Management strategies and plans, http://www.gwpforum.org/gwp/library/Tec_brief_3_Monitoring.pdf
Goldstein, H. (1999). Multilevel statistical models. London: Arnold Publisher. http://www.arnoldpublishers.com/support/goldstein.htm.
Green, D. M., & Swets, J. A. (1966) Signal detection theory and psychophysics. New York: Wiley.
Greenley, J. R., Greenberg, J. S., & Brown, R. (1997). Measuring quality of life: A new and practical survey instrument. Social Work, 42, 244–254.
Guilford, J. P. (1954). Psychometric methods. New York/London: McGraw-Hill.
Hagerty, M. R., & Land, K. C. (2007). Constructing summary indices of quality of life: A model for the effect of heterogeneous importance weights. Sociological Methods and Research, 35, 455–496.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River: Prentice-Hall.
Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory (Measurement methods for the social sciences series, Vol. 2). London: SAGE.
Hanley, J. A., & McNeil, B. J. (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143: 29–36.
Headey, B., Veenhoven, R., & Wearing, A. (1991). Top-down versus bottom-up theories of subjective well-being. Social Indicators Research, 24, 81–100.
Horn, R. V. (1993). Statistical indicators. Cambridge: Cambridge University Press.
Howell, R. D., Breivik, E., & Wilcox, J. B. (2007). Reconsidering formative measurement. Psychological Methods, 12, 205–218.
Hox, J. J. (1995). Applied multilevel analysis. Amsterdam: TT-Publikaties.
Jamison, D., & Sandbu, M. (2001). WHO ranking of health system performance. Science, 293, 1595–1596.
Kalmijn, W. M., & Veenhoven, R. (2005). Measuring inequality of happiness in nations: In search for proper statistics. Journal of Happiness Studies, 6, 357–396.
Kim, J.-O., & Mueller, C. W. (1989a). Introduction to factor analysis: What it is and how to do it (Sage university paper series on quantitative applications in the social sciences, series no. 07–013). Newbury Park: Sage.
Kim, J.-O., & Mueller, C. W. (1989b). Factor analysis: Statistical methods and practical issues (Sage university paper series on quantitative applications in the social sciences, series no. 07–014). Newbury Park: Sage.
Kozma, A., Stone, S., Stones, M. J., Hannah, T. E., & McNeil, K. (1990). Long- and short-term affective states in happiness: Model, paradigm and experimental evidence. Social Indicators Research, 22, 119–138.
Krieger, N. (2002). A glossary for social epidemiology. Epidemiological Bullettin, 23(1). http://www.paho.org/English/sha/be_v23n1-glossary.htm.
Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling (Sage university paper series on quantitative applications in the social sciences, series no. 07–011). Newbury Park: Sage.
Lance, C. E., Mallard, A. G., & Michalos, A. C. (1995). Tests of the causal directions of globallife facet satisfaction relationships. Social Indicators Research, 34, 69–92.
Land, K. C. (1971). On the definition of social indicators. The American Sociologist, 6, 322–325.
Land, K. C. (1975). Social indicator models: An overview. In K. C. Land & S. Spilerman (Eds.), Social indicator models (pp. 5–36). New York: Russell Sage.
Land, K. C. (2000). Social indicators. In Borgatta, E. F., & Montgomery, R. V (Eds.), Encyclopedia of sociology, rev edn. New York: Macmillan
Lazarsfeld, P. F. (1958). Evidence and inference in social research. Daedalus, 87, 120–121.
Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton M Company.
Lis, A., & Sambin, M. (1977). Analisi dei cluster. Padova: CLEUP.
Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data (Wiley Series in Probability and Mathematical Statistics). New York: Wiley.
Long, J. S. (1993a). Confirmatory factor analysis. A preface to LISREL (Sage university paper series on quantitative applications in the social sciences, series no. 07–033). Newbury Park: Sage.
Long, J. S. (1993b). Covariance structure models. An introduction to LISREL (Sage university paper series on quantitative applications in the social sciences, series no. 07–034). Newbury Park: Sage.
Lord, F. M. (1974). Estimation of latent ability and item parameters when there are omitted responses. Psychometrika, 39, 29–51.
Lord, F. M. (1984). Standard errors of measurement at different ability levels. Journal of Educational Measurement, 21, 239–243.
Louviere, J. J. (1988). Analyzing decision making: Metric conjoint analysis. Newbury Park: Sage.
Ludlow, L. H., & Haley, S. M. (1995). Rasch model logits: Interpretation, Use and trasformation. Educational and Psychological Measurement, 55, 967–975.
Lyubomirsky, S., King, L. A., & Diener, E. (2005). The benefits of frequent positive affect: Does happiness lead to success? Psychological Bulletin, 131, 803–855.
Maggino, F. (2003). Method effect in the measurement of subjective dimensions. Firenze: Firenze University Press, Archivio E-Prints.
Maggino, F. (2004a). La misurazione nella ricerca sociale. Teorie, strategie, modelli. Firenze: Firenze University Press, Archivio E-Prints.
Maggino, F. (2004b). I modelli di scaling. Confronto tra ipotesi complesse per la misurazione del soggettivo. Firenze: Firenze University Press, Archivio E-Prints.
Maggino, F. (2005a). L’analisi dei dati nell’indagine statistica. Firenze: Firenze University Press.
Maggino, F. (2005b). The importance of quality-of-life dimensions in Citizens’ preferences: An experimental application of conjoint analysis. Firenze: Firenze University Press, Archivio E-Prints.
Maggino, F. (2007). La rilevazione e l’analisi statistica del dato soggettivo. Firenze: Firenze University Press.
Maggino, F. (2008a). Choice of weights for subjective variables, invited paper at the “Conference on Composite Score” (ESADA, Universitat Ramon Llull, Barcelona, 14–15 February 2008), Chair: Prof. W.Saris
Maggino, F. (2008b). Towards more participative methods in the construction of composite indicators. The case of obtaining weights: from “objective” to “subjective” approaches, invited paper presented at the International Seminar on “Involving citizens/communities in measuring and fostering well-being and progress: towards new concepts and tools”, Council of Europe (Strasbourg, 27–28 November, 2008).
Maggino, F. (2009). Towards more participative methods in the construction of social indicators: survey techniques aimed at determining importance weights, paper to be presented at the 62nd conference of the World Association for Public Opinion Research “Public Opinion and Survey Research in a Changing World” (Swiss Foundation for Research in Social Sciences – University of Lausanne – 11–13 September 2009 – Lausanne – Switzerland).
Maggino, F. (forthcoming). Obtaining weights: from objective to subjective approaches in view of more participative methods in the construction of composite indicators.
Maggino, F. & Ruviglioni, E. (2008a). Choice of subjective weights for subjective variables. Identifying subjective/individualized weights for comparing well-being among group and individuals, paper presented at the session on “Social Indicators” organized by Heinz-Herbert Noll (GESIS-ZUMA, Mannheim) at the VII International Conference on Social Science Methodology (1–5 September 2008, Campus di Monte Sant’Angelo, Naples).
Maggino, F. & Ruviglioni, E. (2008b). Choice of subjective weights for subjective variables. Identifying subjective/individualized weights for comparing well-being among group and individuals, paper presented at the IV International Conference on Quality of Life Research – QOL 2008 – “Quality of Life Improvement through Social Cohesion”, Conference organized by the Department of Statistics of Wroclaw University of Economics (15–18 September 2008, Wroclaw).
Maggino, F. & Ruviglioni, E. (2009) Obtaining weights: from objective to subjective approaches in view of more participative methods in the construction of composite indicators, paper presented at the Seminar on “New Techniques and Technologies for Statistics (NTTS)” – EUROSTAT (18–20 February 2009 – Brussels – Belgium).
Malhotra, N. K. (1996). Marketing research: An applied orientation. Englewood Cliffs: Prentice-Hall International, Inc.
Mallard, A. G. C., Lance, C. E., & Michalos, A. C. (1997). Culture as a moderator of overall life satisfaction relationships. Social Indicators Research, 40, 259–284.
Marradi, A. (1981). Factor analysis as an aid in the formation and refinement of empirically useful concepts. In E. F. Borgatta & D. J. Jackson (Eds.), Factor analysis and measurement in sociological research. London: SAGE.
Maslow, A. H. (1954). Motivation and personality. New York: Harper and Brothers.
Max-Neef, M. (1992). Development and human needs. In P. Ekins & M. Max-Neef (Eds.), Real life economics: Understanding wealth creation (pp. 97–213). London: Routledge.
McDonald, R. P. (1989). Future directions for item response theory. International Journal of Educational Research, 13, 205–220.
Menard, S. (1991). Longitudinal research (Sage university paper series on quantitative applications in the social sciences, series no. 07–076). Newbury Park: Sage.
Michalos, A. C. (1985). Multiple discrepancies theory (MDT). Social Indicators Research, 16, 347–413.
Michalos, A. C. (1992). Use and abuses of social indicators. Sinet 32.
Michalos, A. C. (2008). Education, happiness and wellbeing. Social Indicators Research, 87, 347–366.
Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2005a). Handbook on constructing composite indicators: Methodology and userguide. OECD, Statistics Working Paper
Nardo, M., Saisana, M., Saltelli, A., & Tarantola, S. (2005b). Tools for composite indicators building. European Commission, EUR 21682 EN, Institute for the Protection and Security of the Citizen, JRC, Ispra.
Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling procedures: Issues and applications. Thousand Oaks: Sage.
Noll, H. -H. (1996). Social indicators and social reporting: The international experience, http://www.ccsd.ca/noll1.html
Noll, H. -H. (2004). Social indicators and indicators systems: tools for social monitoring and reporting, Paper presented at OECD, World Forum “Statistics, knowledge and policy”, Palermo, 10–13 November 2004.
Noll, H. -H. (2009). Measuring and monitoring the quality of life, Lecture at the Università degli Studi di Firenze, April 23–24, 2009. http://www.gesis.org/forschung-lehre/veranstaltungen/veranstaltungs-archiv/zentrum-fuer-sozialindikatorenforschung/quality-of-life/
Nussbaum, M., & Glover, J. (1995). Women, culture, and development: A study of human capabilities. Oxford: Oxford University Press.
Organisation for Economic Cooperation and Development. (2007). Istanbul Declaration, II OECD World Forum on “Statistics, Knowledge and Policy”, June, 27–30, Istanbul. http://www.oecd.org/document/51/0,3343,en_21571361_31938349_37115187_1_1_1_1,00.html
Patel, S., Hiraga, M, Wang, L., Drew, D., & Lynd, D. (2003). A framework for assessing the quality of education statistics, World Bank, Development Data Group and UNESCO, Institute for Statistics.
Rampichini, C., & Schifini, S. (1998). A hierarchical ordinal probit model for the analysis of life satisfaction in Italy. Social Indicators Research, 44, 5–39.
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park: Sage.
Rubin, D. B. (1987). Multiple imputation for nonresponse in survey (Wiley Series in Probability and Mathematical Statistics). New York: Wiley.
Rupp, A. A., & Zumbo, B. D. (2003). Which model is best? robustness properties to justify model choice among unidimensional IRT models under item parameter drift. Alberta Journal of Educational Research, 49, 264–276.
Rupp, A. A., & Zumbo, B. D. (2006). Understanding parameter invariance in unidimensional IRT models. Educational and Psychological Measurement, 66, 63–84.
Rupp, A. A., Dey, D. K., & Zumbo, B. D. (2004). To bayes or not to bayes, from whether to when: Applications of Bayesian methodology to modeling. Structural Equation Modeling, 11, 424–451.
Russell, L. B., Hubley, A. M., Palepu, A., & Zumbo, B. D. (2006). Does weighting capture what’s important? Revisiting subjective importance weighting with a quality of life measure. Social Indicators Research, 75, 141–167.
Sadocchi, S. (1981). Manuale di analisi statistica multivariata per le scienze sociali. Milano: Franco Angeli Editore.
Saisana, M., Saltelli, A., & Tarantola, A. (2005). Uncertainty and sensitivity techniques as tools for the analysis and validation of composite indicators. Journal of the Royal Statistical Society A, 168, 1–17.
Sakitt, B. (1973). Indices of discriminability, Nature, 241: 133–134.
Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity analysis in practice. A guide to assessing scientific models. Chichester: Wiley, http://webfarm.jrc.cec.eu.int/uasa/doc/forum-tutorial/WU082-FM.pdf.
Saris, W. E., & Stronkhorst, L. H. (1990). Causal modelling in nonexperimental research. An introduction to the LISREL approach. Amsterdam: Sociometric Research Foundation.
Scherpenzeel, A., & Saris, W. (1996). Causal direction in a model of life satisfaction: The top-down/bottomup controversy. Social Indicators Research, 38, 161–180.
Sen, A. (1993). Capability and well-being. In M. Nussbaum & A. Sen (Eds.), The quality of life (pp. 30–53). Oxford: Clarendon.
Sharpe, A., & Salzman, J. (2004). Methodological choices encountered in the construction of composite indices of economic and social well-being. Ottawa: Center for the Study of Living Standards.
Sijtsma, K., & Molenaar, I. W. (2002). Introduction to nonparametric item response theory (Measurement methods for the social sciences series, Vol. 5). London: SAG.
Sinden, J. A. (1982). Application of quality of life indicators to socioeconomic problems: An extension of Liu’s method to evaluate policies for 26 Australian towns. American Journal of Economics and Sociology, 41, 401–420.
Simpson, A. J., & Fitter, M. J. (1973) What is the best index of detectability? Psychological Bulletin, 80: 481–488.
Sirgy, M. J., Michalos, A. C., Ferriss, A. L., Easterlin, R. A., Patrick, D., & Pavot, W. (2006). The quality-of-life (QOL) research movement: Past, present, and future. Social Indicators Research, 76, 343–466.
Stiglitz J. E., Sen, A., & Fitoussi, J.-P. (Eds.) (2009) Report by the commission on the measurement of economic performance and social progress, Paris. http://www.stiglitz-sen-fitoussi.fr/en/index.htm
Stoker, T. M. (1993). Empirical approaches to the problem of aggregation over individuals. Journal of Economic Literature, 31, 1827–1874.
Stones, M. J., Hadjistavropoulos, T., Tuuko, H., & Kozma, A. (1995). Happiness has trait-like and state-like properties: A reply to Veenhoven. Social Indicators Research, 36, 129–144.
Sullivan, J. L., & Feldman, S. (1981). Multiple indicators. An introduction (Sage university paper series on quantitative applications in the social sciences, series no. 07–015). Newbury Park: Sage.
Swaminathan, H., & Gifford, J. A. (1982). Bayesian estimation in the Rasch model. Journal of Educational Statistics, 7, 175–192.
Swaminathan, H., & Gifford, J. A. (1985). Bayesian estimation in the two-parameter logistic model. Psychometrika, 50, 349–364.
Swaminathan, H., & Gifford, J. A. (1986). Bayesian estimation in the three-parameter logistic model. Psychometrika, 51, 589–601.
Tarantola, S., Jesinghaus, J. M., & Puolamaa, M. (2000). Global sensitivity analysis: A quality assurance tool in environmental policy modelling. In A. Saltelli, K. Chan, & M. Scott (Eds.), Sensitivity analysis (pp. 385–397). New York: Wiley.
Torgerson, W. S. (1958). Theory and methods of scaling. New York/London/Sydney: Wiley.
Tucker, L., & MacCallum, R. (1993). Exploratory factor analysis, Book Manuscript, Retrieved in 2008, from: http://www.unc.edu/~rcm/book/factornew.htm
United Nations Development Programme. (2007). Human development report 2007. New York: Oxford University Press.
Veenhoven, R. (2002). Why social policy needs subjective indicators. Social Indicators Research, 58, 33–45.
Veenhoven, R. (2005). Inequality of happiness in nations. Journal of Happiness Studies, 6, 351–355.
Veenhoven, R. (2009). Well-being in nations and well-being of nations. Is there a conflict between individual and society. Social Indicators Research, 91, 5–21.
Werts, C. E., Linn, R. L., & Jöreskog, K. G. (1974). Quantifying unmeasured variables. In H. M. Blalock (Ed.), Measurement in the social sciences: Theories and strategies. Chicago: Aldine Publishing Company.
Yoon, K. P., & Hwang, C.-L. (1995). Multiple attribute decision making (Sage university paper series on quantitative applications in the social sciences, series no. 07–104). Thousand Oaks: Sage.
Zapf, W. (1975). Systems of social indicators: Current approaches and problems. International Social Science Journal, 27, 479–498.
Zapf, W. (1984). Individuelle Wohlfahrt: Lebensbedingungen und Wahrgenommene Lebensqualität. In W. Glatzer e & W. Zapf (Eds.), Lebensqualität in der Bundesrepublik (pp. 13–26). New York/Campus: Frankfurt a. M.
Zumbo, B. D. (2007). Validity: Foundational issues and statistical methodology. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics (Psychometrics, Vol. 26, pp. 45–79). Boston: Elsevier.
Zumbo, B. D. (2009). Validity as contextualized and pragmatic explanation, and its implications for validation practice. In R. W. Lissitz (Ed.), The concept of validity: Revisions, new directions and applications (pp. 65–82). Charlotte: Information Age Publishing.
Zumbo, B. D., & Rupp, A. A. (2004). Responsible modeling of measurement data for appropriate inferences: Important advances in reliability and validity theory. In D. Kaplan (Ed.), The SAGE handbook of quantitative methodology for the social sciences (pp. 73–92). Thousand Oaks: Sage.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Netherlands
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-94-007-2421-1_10
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-2420-4
Online ISBN: 978-94-007-2421-1
eBook Packages: Humanities, Social Sciences and LawSocial Sciences (R0)