Conference Agenda

This presentation will discuss how many theoretical constructs lack a stable, validated multi-item scale and are therefore predominantly measured by one or more single-item questions in a questionnaire, and are often compared across many countries regardless of whether the item is actually culturally comparable across countries. Although there are obvious practical, financial, and feasibility reasons for using single-item measures instead of multi-item scales in international questionnaires (e.g., survey space, interview time, etc.), methodologists must always emphasize that “single-item measures are rather imprecise, do not have high reliability or high construct validity, and do not allow for controlling for measurement error” (Davidov et al., 2018).

The aims of this presentation are to (a) present variable ways of measuring subjective social status and general well-being using single and/or multiple (but separate) items in large-scale cross-national surveys, (b) empirically test their measurement invariance across countries and time points, and (c) discuss the kinds of comparisons that can be made given these results. Despite numerous secondary analyses of subjective class and general well-being differences across countries, evidence on the comparability of individual indicators is still lacking, potentially leading to misinterpretations of social inequalities and limiting cross-country comparisons of these constructs.

I used a post-hoc approach to assess the measurement invariance of single-item measures across countries based on the synthesis of an auxiliary multi-item instrument from several theoretically and empirically appropriate single-item measures presented in the questionnaire. By examining the invariance of the single-item measures across countries and comparing the results across survey programs, I will be able to determine whether different single questions and scale designs lead to different levels of comparability. To do this, I used over 50 data samples from the World Values Survey, International Social Survey Program, European Values Study, European Social Survey, European Quality of Life Survey, and Eurobarometer from 1976 to 2019. The latest Bayesian approximation approach to measurement invariance testing is applied because, among other things, it provides the opportunity to measure invariance across a large number of groups and can reveal the extent of non-invariance of specific items.

The results show that some (single-)item measures are not invariant across countries and/or survey programs. Thus, only partial scalar approximate measurement invariance holds for them, and their average or the relationship between them and other variables should not be compared across countries. However, when the items were treated together as an (auxiliary) latent variable, this composite measure showed approximate scalar invariance across countries and survey rounds. Based on this, I would urge that (at least) all three questions used to measure theoretical constructs be included in the international questionnaire for future valid research on social class categorization and well-being.

Research into subjective wellbeing and its social-ecological determinants shows continued debate on the question whether rural areas have higher levels of SWB than urban areas. Besides findings suggesting potential variations by countries and for specific social subgroups, most studies on rural-urban differences have relied on single-item questions measuring the evaluative dimension of SWB (i.e., assessing life satisfaction). In contrast, sociological theories of rural-urban differences in SWB suggest domain specific variation. For instance, higher degrees of Gemeinschaft (e.g., social cohesion and collective efficacy) in rural areas might lead to higher social wellbeing or basic psychological need satisfaction. To contribute to this ongoing debate, we use data (about forty survey questions and over 50,000 total cases) from the 2012 European Social Survey module on SWB to develop a fine-grained model capturing five major dimensions of SWB (i.e., evaluative, affective, basic psychological need satisfaction, mental resources, and social wellbeing). We construct Mazziotta-Pareto Indices to assess SWB not only across countries but also along the rural-urban continuum and for specific social subgroups. In line with past research, we find support for rural-urban differences in SWB (i.e., rural areas show higher degrees of SWB). However, our analyses help to refine this relationship by highlighting that this is not a clear-cut linear process but SWB differs by types of rural-urban areas and SWB dimension. We, moreover, find that without the consideration of SWB subdimensions, we might receive a distorted picture of rural-urban differences for specific social groups (e.g., some subgroup differences are not universal but bound to specific subdimensions of SWB).

In this communication we present a research proposal to measure the current Quality of Life (2024) in four regions of Extremadura (Spain). Two of these regions are urban (Plasencia and Mérida) and the other two are rural (Valle del Jerte and La Serena). To do this, we will use a Multidimensional Quality of Life Indicator (IMCV), of an experimental nature, made up of nine basic dimensions: material living conditions, health, work, education, social relations, insecurity, governance and participation social, environment and subjective well-being, among which fifty-five (55) indicators on Quality of Life are distributed, used and validated by the National Institute of Statistics (INE) of Spain to measure multidimensional phenomena, applying the Mazziota-Method. Adjusted Pareto (AMPI), to be able to make comparisons in space and/or time. These 55 indicators (IMCV) will be complemented with other additional indicators from the Active Population Survey (EPA) of Spain and the European Social Survey (ESS, Round 10-2020). An additional objective of the research is to weigh the impact of each of these nine dimensions on Quality of Life. To do this, we will use the opinions and comments generated by a panel of experts and key informants, on the differences observed in the four regions that make up the universe of the research.

Welfare is an old concept that humans have been trying to reach throughout history. First and foremost, the family was the primary source of welfare. However, its role has changed with the inclusion of the state and market. Esping-Andersen (1990) explains the welfare state regime as a mix of state, family, and market which are seen as the main suppliers of welfare against social risks. He classifies welfare state regimes into three different types according to their relationship with the state, market and family.In addition to these three pillars of the welfare state regimes, Non-Governmental Organizations (NGOs) contribute to welfare distribution by collaborating with the other three pillars to provide services, raise awareness and improve people’s well-being.

In this study, the life satisfaction of individuals will be analyzed since in a welfare society people are expected to be more satisfied with their lives. Our hypothesis is volunteering through NGOs enhances life satisfaction in four types of welfare state regimes. Sweden as social democratic, Austria as continental European, United Kingdom as liberal, and finally Italy as southern European will be investigated by using the data of European Social Survey (ESS) wave ten which is conducted from 2020 to 2022. To investigate this hypothesis, logistic regression will be used in four different types of welfare state regimes. The impact of volunteering through nonprofit organizations on life satisfaction will be analyzed with other control variables like gender, age, education level, marital status, employment status, subjective health, subjective assessment of income, and place of residence. Results show that voluntary giving has a significant positive effect on life satisfaction in these welfare states.

Since ancient times, scholars have pondered the factors contributing to human happiness. Early on, economists correlated well-being with the possession of material goods (Stiglitz et al., 2009), leading to Gross Domestic Product (GDP) becoming a widely used indicator for assessing a country's well-being and its policies. However, contemporary perspectives suggest that this approach is overly narrow (Costanza et al., 2009; Diener and Seligman, 2004; Diener and Suh, 1997; Stiglitz et al., 2009). The conceptual gap between economic growth and well-being has never been more apparent. Developed countries now grapple with new political and social challenges, including climate change, refugees, environmental and water pollution, terrorism, and renewable energy. There is a growing awareness in each country that mere economic growth is insufficient to address these complex issues (Noll and Zapf, 1994).

These considerations result in the awareness that additional properties should be investigated in order to evaluate individual well-being. The rationale is twofold. First, the economic growth can be negatively correlated with fundamental dimensions like the amount of free time, the quality of atmosphere and income equality. Second, different people have different capabilities in turning resources into well-being (Sen 1985; Nussbaum 2011).

While numerous studies have examined the impact of economic aggregate conditions on subjective well-being, scant attention has been devoted to exploring the social macro determinants of individual well-being.

This study assess the effect of a macro societal factor, social cohesion, on individual well-being controlling the relationships for other macro-economic dimensions and for individual factors. In addition we also tested the hypothesis that cohesion exerts a moderating effect on the relationship between income and quality of life.

To perform our analysis we used data from the ESS Round 6 which included 29 countries and almost 50000 respondents. The social cohesion concept was measured using a multilevel structural equation modelling approach. To test our hypothesises, we employed a series of multilevel regression models. The models show that, controlling for other macro factors – GDP, Life Expectancy, Gini coefficient and Homicide rate – and individual-level variables, Cohesion exerts a positive effect on subjective quality of life. The model also points out that country’s economic conditions (measured by GDP) do not affect quality of life when we control this relationship for social cohesion. Finally, we also shows that the positive effect of income on quality of life is moderated by cohesion. In other words, income is a relatively less important factor in determining quality of life in countries with higher levels of cohesion