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Bridging the Gap: Overcoming Data Difficulties During 40 Years of Measuring Well-Being in The Netherlands

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Abstract

For more than 40 years the Netherlands Institute for Social Research|SCP has studied the quality of life of the Dutch population. A characteristic element of that research is the use of time series. However, maintaining a time series over such a long period is not easy. All manner of problems arise, such as indicators no longer being relevant (who still has a black and white television?) or the changing importance that is attached to particular topics (sport is more important today than it was in 1974). The method of data collection can also change. In this article we take the perspective of a specific measurement to look at the changes that have taken place over the last 40 years and how those changes have been accommodated. We do this using the SCP Life Situation Index, which measures the objective quality of life of Dutch citizens. The conclusion is that it is possible to construct a long-term time series, but that it is not always possible to interpret changes from year to year: the perspective has to focus primarily on long-term developments.

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Notes

  1. The standard deviation is approximately 13 in 2013.

  2. More detailed information can be found on the SCP website (only in Dutch): http://www.scp.nl/Onderzoek/Bronnen/Beknopte_onderzoeksbeschrijvingen/Culturele_veranderingen_in_Nederland_CV and http://www.scp.nl/Onderzoek/Bronnen/Beknopte_onderzoeksbeschrijvingen/SCP_Leefsituatie_index_SLI. The response rate in the 2010 wave of Cultural Changes in the Netherlands was 57 %. 78 % of these respondents took part in the SLI survey.

  3. With other techniques, where there is no possibility to define clusters, such as factor analysis, the four indicators for housing acquire greater weights than the two indicators for sport. Although the domains are equally weighted within OVERALS, the indicators and categories are not: “Analogously to the situation in multiple regression and canonical correlation analysis, OVERALS focuses on the relationships between sets; any particular variable contributes to the results only inasmuch as it provides information that is independent of the other variables in the same set” (SPSS 2001). In addition, nominal indicators such as type of dwelling can also be included.

  4. More precisely: “The goal is to explain as much as possible of the variance in the relationships among two sets of numerical variables in a low dimensional space. Initially, the variables in each set are linearly combined such that the linear combinations have a maximum correlation. Given these combinations, subsequent linear combinations are determined that are uncorrelated with the previous combinations and that have the largest correlation possible. The optimal scaling approach expands the standard analysis in three crucial ways. First, Overals allows more than two sets of variables. Second, variables can be scaled as either nominal, ordinal, or numerical. As a result, nonlinear relationships between variables can be analysed” (SPSS 1999, p. 37).

  5. In all test scenarios the correlation with the original index is at least 0.98.

  6. A partially parallel data collection method (half in the old way and half using the new system) can provide some insight into this. Unfortunately, this was not done with the data collection for the Life Situation Index.

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Correspondence to Jeroen Boelhouwer.

Appendix: List of Indicators Included in the SCP Life Situation Index

Appendix: List of Indicators Included in the SCP Life Situation Index

  • Health

    • Impeded in daily activities due to a serious disorder, long-term illness or disability (greatly impeded; slightly impeded; not impeded; has no illness, disorder or disability);

    • Impeded in leisure activities due to a serious disorder, long-term illness or disability (greatly impeded; slightly impeded; not impeded; has no illness, disorder or disability).

  • Housing

    • Dwelling type (detached house; attached house; apartment; flat; retirement dwelling; youth fo student accommodation; other);

    • Number of bedrooms, living rooms, study or work rooms;

    • Area of living room;

    • Owner-occupied or rented dwelling.

  • Sociocultural activities

    • Number of visits to cultural institutions (classical concert, pop concert, opera, theatre, ballet, cabaret/light entertainment, musical, film, dance evening or house party, museum);

    • Membership of associations (singing club, music society or drama club; sports club; hobby club; political organisation; trade union, employees’ or employers’ organisation; library; other association);

    • Number of hobbies (e.g. playing music, singing, ballet, amateur dramatics, painting or drawing, handicrafts, DIY, collecting, computer activities, walking or cycling).

  • Social participation

    • Does voluntary work (singing, music or theatre club; sports club; hobby club; political organisation; trade union, employees’ or employers’ organisation; religious or ideological organisation; school, crèche or playgroup (e.g. parents’ committee, school board or other help at school); help for neighbours, the elderly, the disabled; organisation with social object (e.g. human rights, nature or animal protection); community association or community centre; other association(s) or organisations);

    • Scale for social isolation (there are people I can talk to; I feel isolated from other people; there are people I can turn to; there are people who really understand me; I’m part of a group of friends; my social contacts are superficial).

  • Sport

    • How many sports played in last 12 months;

    • How often have you taken part in sport in the last 12 months (not; less than once per month; 1-3 times per month; once per week; twice per week; three times per week; four times per week or more).

  • Holidays

    • How often have you been on holiday during the last 12 months (not; once; more than once);

    • Have you been on a foreign holiday in the last 12 months?

  • Ownership of durable consumer goods

    • Hobby equipment (DVD player; personal computer);

    • Household goods (microwave; dishwasher).

  • Mobility

    • Car ownership;

    • Public transport subscription.

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Boelhouwer, J. Bridging the Gap: Overcoming Data Difficulties During 40 Years of Measuring Well-Being in The Netherlands. Soc Indic Res 130, 129–145 (2017). https://doi.org/10.1007/s11205-015-1122-3

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