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Measuring Stability and Change: Methodological Issues in Quality of Life studies

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An Erratum to this article was published on 28 January 2016

Abstract

The aim of the paper is to show how the variety of approaches to study social change may result in a challenging complexity for the social scientist, starting from the difficulty of defining the concept of “change” itself and managing it through observed data. This is particularly true in presence of complex phenomena, such as those defining and composing the quality of life. What should be pointed out is that quality of life studies not only are focused on the present time but have also long term perspectives. This represents the link between studies on quality of life and forecasting. When applied to the field of quality of life, the typical logical approach to forecasts, based upon inferential statistics, could reveal its limits. Those limits are related to different aspects: e.g., the forms of relationships between different aspects of the phenomenon, which can be linear and non-linear; the dimensionality of phenomenon, which can turn out to be very complex; the causality, which could be direct or indirect; the entity of change, which implies the idea that also small change can have great impact; the perspective of observation, which can be internal or external and local or global. Consequently, the study of change related to quality of life needs, in addition to the traditional statistical tools as well as the tradition of social indicators, a different approach. Although the Futures Studies are not a proper science, nevertheless their approach to social research may ensure the requested accuracy of a scientific forecasting process.

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Notes

  1. Statistics deal with—among many issues—identifying regularities in collected information (data). Those regularities can be observed in terms of tendencies when a certain phenomenon is observed in the course of time, or in terms of relationships, when regular convergences or divergences are observed among phenomena.

    Observing regularities allows interpretations and explanations to be hypothesized. Consistently with the acquired knowledge, which can be formalized in a model, it is possible to speculate about how the phenomenon will turn up in the future. In other words, in the statistical context, models allow forecasts (projections) to be formulated also in the perspective of taking decisions (which could influence trends of certain events).

    In statistical model definition, three main approaches can be identified, (1) from observing a part (estimation), (2) from defining a hypothesis (testing through falsifiability), and (3) from observing the past (forecasts).

    Applying such approaches lead to a statistical decision which is always expressed in probabilistic terms. In other words, the statistical forecast always contains an uncertainty component, expressed in terms of probability.

  2. Notice that also ordinary least squares method is based on a deterministic formulation for an equation defining a line (Brown 1995).

  3. The term ‘map’ in place of ‘function’ is usually applied with reference to difference equations that associate paired data for discrete time. The general form for these functions is logistic.

  4. The concept of close points of time in a data series refers to time variability of phenomena under study, especially in social research.

  5. The study of qualitative differences between classes of graphs connected by continuous transformations is part of topology.

  6. A typical example of absorbing state is death.

  7. In statistical terms, this requires (Goldstein 1979; Firebaugh 1997; Bryk and Raudenbush 1992):

    • Identifying an interpolating line, connecting points for the observed group (individual change model),

    • Estimating model parameters (slope and intercept) in order to identify a general modification of characteristic and to analyze differences among individuals (general change model).

  8. As reported below, the combination of waves and number of involved variables defines different kind of model; the simplest one is the two-wave two-variable (2W2V) model.

  9. Many interesting works analyze data obtained by repeated designs in order to describe national/regional trends.

  10. Traditionally, these kinds of data apply to

    • Survival and reliability studies, when the observation on an individual begins when the characteristics of interest is first diagnosed;

    • Incidence studies, when the duration is measured from the origin (rate of occurrence, obtained by longitudinal follow up);

    • Prevalence studies, when the interest is on the frequency in a population at a given point of time (in a fixed population, prevalence = incidence X duration).

    • Survival and incidence are modeled by intensity whereas prevalence by probability.

  11. If the systematic variance of a given observation depends upon preceding observations the time-series defines a Markoff chain (the order of which depends upon the number of preceding dependence observations).

  12. In order to avoid the arbitrary decision involved in the previously presented approaches, yielding arbitrary approximations, the data matrix is sliced in three different direction; each arrangement is a table in which one dimension represents one of the original matrix dimension and the other represents the Cartesian product of the other two dimensions; the resulting three tables are transposed in such a way that the rows may be interpreted as observations and the columns as variables.

  13. The assumptions concern:

    • Experimental conditions: each individual is assumed to be randomly assigned to treatments and to stay in the same treatment group during the experiment;

    • Statistical characteristics of data: normal distribution of observed variables and equal variances and covariances between observations over time for all individuals.

  14. If the change in both variables occurs in the same period, there are different possible explanations (Menard 1991):

    1. (a)

      The two variable measure the same thing,

    2. (b)

      The two variables are spuriously related, having a common cause producing changes in both,

    3. (c)

      The length of measurement period does not allow to separate the two changes.

  15. The debate on the relationship between explanation and forecasting (some authors use the term “prediction”, others use “prevision”) is an important part of the wider debate on scientific explanation which started from a paper by Hempel and Oppenheim (1948), considered the foundation of scientific explanation view. Among the authors who discussed the topic of forecasting in this context, it should be cited Scriven (1959), Goodman (1959), Nagel (1961), Hempel (1965, 1977), Salmon (1971), and Coffa (1974). For a very interesting history of the debate on scientific explanation, see Salmon (Salmon 1989).

  16. Generally, the scenario analysis considers the following stages: (1) identifying critical and external factors (social, technological, economic, environmental, and political); (2) identifying alternative futures (forecasts); (3) developing strategies (decisions). The first stage represents the crucial step in defining admissible scenarios.

  17. One of the most important theorists of FS was Bertrand de Jouvenel, philosopher who—in the Fifties of XX century—founded the periodical Futuribles and published L’art de la conjecture in 1964: in this work, he theorized the difference between desirables (considered as utopia), plausible, probable, and possible futures (de Jouvenel 1964). Gaston Berger, another important French theorist, stressed the importance of the link between policy makers and FS and theorized the politically active FS.

    American theorists gave a great contribution to the discipline by developing specific techniques (scenarios, Delphi, simulation systems) especially thanks to the RAND Corporation contribution.

    Also Italy gave important contribution to FS through prestigious personalities like Aurelio Peccei (former FIAT manager and founder of the Club of Rome), Pietro Ferraro (industrial who, inspired by the French Futuribles, founded the Italian journal Futuribili), Eleonora Barbieri Masini (sociologist who coordinated the World Futures Studies Federation for years and very important scientific researches based on Futures Studies, such as “Household, Gender, and Age”, which will described further below).

    Beyond the nationality of those who contributed to the discipline, FS have indeed the natural vocation to interconnect countries and people. For example, the Club of Rome included members from Russia and America (in spite of the Cold War).

    The Club of Rome’s report The Limits to Growth, developed by M.I.T. scientists (Meadows et al. 1972) was indeed one of the most debated scientific report on environment risk of all times. It is clearly a non-trivial approach to social research indeed: the underlying issues are complex and involve many themes related to methodology of social sciences.

  18. Sections 1 and 2 describe many of the statistical techniques FS utilize. Furthermore, the discipline developed also its own techniques: the most well-known are with no doubt Delphi Method and scenarios.

  19. According to Weber, the scientists must explain their ethical and political beliefs and their opinions; at the same time, these value-aspects must never influence, in any way, their scientific work. The value-freedom can be a very faint boundary even in average social research: a thin line clearly crossed by FS researchers. Die Obiectivität socialwissenschaftlicher und sozialpolitischer Erkenntnis (Weber 1904) and Wissenschaft als beruf (1917–1919).

  20. For an analysis of subjective well-being indicators, see: http://www.fqts.org/dati/doc/128/doc/191.pdf.

  21. The identified indicators include also subjective data able to picture reality also in this perspective. ISTAT has a long experience in collecting subjective data. The main source of ISTAT’S subjective data is represented by the Multipurpose Surveys System, which provide that Annual Survey on Aspects of Everyday Life.

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Authors and Affiliations

  1. Department of Statistics, Informatics, Applications “G.Parenti” (DiSIA), University of Florence, Florence, Italy

    Filomena Maggino

  2. National Institute of Statistics – ISTAT, Rome, Italy

    Carolina Facioni

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  1. Filomena Maggino
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  2. Carolina Facioni
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Correspondence to Carolina Facioni.

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An erratum to this article is available at http://dx.doi.org/10.1007/s11205-016-1241-5.

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Maggino, F., Facioni, C. Measuring Stability and Change: Methodological Issues in Quality of Life studies. Soc Indic Res 130, 161–187 (2017). https://doi.org/10.1007/s11205-015-1129-9

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