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AbstractBackgroundIn the digital age, contacting a public official is a direct, low‐cost means of influence, but most cross‐national surveys ignore it.ObjectiveWe go in‐depth on "contact" to define it, present how it has appeared in major cross‐national surveys since the 1960s, and how survey respondents might understand it today. We then explore its popularity and determinants in Europe across time.MethodsWe use the Survey Data Recycling project's Cotton File and the European Social Survey (ESS) 2002 and 2018.ResultsOf nine major international survey projects that have this item, only ESS regularly includes it. Across Europe, the popularity of contact did not change much, and the factors that associate with generalized contact in 2002 are the same as in 2018.ConclusionData producers should test the meaning of contact among respondents and differentiate between generalized and particularized forms.

This article focuses on assessing item comparability in cross-national surveys by asking probing questions in Web surveys. The "civil disobedience" item from the "rights in a democracy" scale of the International Social Survey Program (ISSP) serves as a substantive case study. Identical Web surveys were fielded in Canada (English-speaking), Denmark, Germany, Hungary, Spain, and the U.S. A category-selection and a comprehension probe, respectively, were incorporated into the Web surveys after the closed-ended "civil disobedience" item. Responses to the category selection-probe reveal that notably in Germany, Hungary, and Spain the detachment of politicians from the people and their lack of responsiveness is deplored. Responses to the comprehension probe show that mainly in the U.S. and Canada violence and/or destruction are associated with civil disobedience. These results suggest reasons for the peculiar statistical results found for the "civil disobedience" item in the ISSP study. On the whole, Web probing proves to be a valuable tool for identifying interpretation differences and potential bias in cross-national survey research.

Two sets of issues are elaborated and reflected on. The first one concerns the distribution of stocks of social capital across European (EU) nations. Using the conventional proxies like trust and associational involvement on the basis of data of cross-national surveys (EVS/WVS and ESS) the main tentative trends and patterns are outlined. The second issue refers to the quality of data. Are the findings and indicators reliable and credible? Are the EVS and other available European surveys a methodologically correct and sufficient source of empirical evidence? Concerning the first issue, it is possible to speak of four groups with regard to the distribution of social capital at the European level. In contrast to authors who argue that there are no large differences in mean scores across the national patterns, this analysis arrives at more differentiated conclusions. Between the first and fourth group a very clear borderline can be drawn while differences between the second and third groups are not so pronounced. In the context of the thematising and critical reflection of the validity and comparability of the empirical evidence some errors and inconsistencies were found. It seems that they appeared partly due to the inappropriate technical design of cross-national surveys and also because of the semantic-cultural as well as institutional factors which have not been paid much attention, partly in the stage of preparing and conducting the survey as well as in the phase of interpreting and generalizing of the data.

"Two sets of issues are elaborated and reflected on. The first one concerns the
distribution of stocks of social capital across European (EU) nations. Using the
conventional proxies like trust and associational involvement on the basis of data of
cross-national surveys (EVS/WVS and ESS) the main tentative trends and patterns are
outlined. The second issue refers to the quality of data. Are the findings and indicators
reliable and credible? Are the EVS and other available European surveys a
methodologically correct and sufficient source of empirical evidence?
Concerning the first issue, it is possible to speak of four groups with regard to the
distribution of social capital at the European level. In contrast to authors who argue that
there are no large differences in mean scores across the national patterns, this analysis
arrives at more differentiated conclusions. Between the first and fourth group a very
clear borderline can be drawn while differences between the second and third groups are
not so pronounced. In the context of the thematising and critical reflection of the
validity and comparability of the empirical evidence some errors and inconsistencies
were found. It seems that they appeared partly due to the inappropriate technical design
of cross-national surveys and also because of the semantic-cultural as well as
institutional factors which have not been paid much attention, partly in the stage of
preparing and conducting the survey as well as in the phase of interpreting and
generalizing of the data." (author's abstract)

Two sets of issues are elaborated and reflected on. The first one concerns the distribution of stocks of social capital across European (EU) nations. Using the conventional proxies like trust and associational involvement on the basis of data from cross-national surveys (European Values Study and European Social Survey as well as from Eurobarometer), the main tentative trends and patterns are outlined. The second set of issues refers to the quality and credibility of data. Concerning the first issue, it is possible to speak of four groups of countries with regard to the distribution of social capital at the European level. In contrast to authors who argue that there are no large differences in mean scores across the national patterns, this analysis arrived at more differentiated conclusions. Between the first and fourth groups, a very clear borderline can be drawn, while differences between the second and third groups are not so pronounced. In the context of the thematizing and critical reflection of the validity and comparability of the empirical evidence, some errors and inconsistencies were found. It seems that they appeared in part due to the inappropriate technical design of cross-national surveys and in part because of the countries-specific semantic-cultural as well as institutional factors, which did receive much attention either in the stage of preparing and conducting the survey or in the phase of interpreting and generalizing of the data.

This contribution provides an overview of the theoretical conceptualisation, empirical operationalisation, and the measurement and coding of education in national and international survey research. In this context, the term "education" refers to the level of education attained by an individual, which must be distinguished from concepts such as competencies, performance at school, and educational disciplines. Because education is often included in statistical models in a merely routine way, emphasis is placed on the connection between the theoretical concept, the indicator, the measurement instrument, and the variable. When doing so, use is made of long-standing social science research on educational returns and educational inequality. A distinction is made between linear, ordinal and categorical concepts of education that have emerged from the different theoretical approaches, and that, to some extent, impose different requirements on data collection and coding. Indeed, it is true to say that there is no consensus as to how education should be conceptualised, measured, compared across countries, and statistically modelled. The contribution does not give a recommendation for a "one size fits all" educational variable that would be appropriate for all studies. Rather, it endeavours to encourage readers to make an informed decision on the measurement of education in the respective educational context and to support them in doing so.

Surveys measuring the same concept using the same measure on the same population at the same point in time should result in highly similar results. If this is not the case, this is a strong sign of lacking reliability, resulting in non-comparable data across surveys. Looking at the education variable, previous research has identified inconsistencies in the distributions of harmonised education variables, using the International Standard Classification of Education (ISCED), across surveys within the same countries and years. These inconsistencies are commonly explained by differences in the measurement, especially in the response categories of the education question, and in the harmonisation when classifying country-specific education categories into ISCED. However, other methodological characteristics of surveys, which we regard as 'containers' for several characteristics, may also contribute to this finding. We compare the education distributions of nine cross-national surveys with the European Union Labour Force Survey (EU-LFS), which is used as benchmark. This study analyses 15 survey characteristics to better explain the inconsistencies. The results confirm a predominant effect of the measurement instrument and harmonisation. Different sampling designs also explain inconsistencies, but to a lesser degree. Finally, we discuss the results and limitations of the study and provide ideas for improving data comparability.

AbstractThe existing cross‐national surveys use various types of scales including 2‐point, 3‐point, 4‐point, and 5‐point scales, and the translations of response statements vary depending on responsible organizations even within the same country. This paper examines how differences in response categories of the agreement scale may impact the distribution of responses in cross‐national surveys and reports the strategies for designing the agreement scale for the East Asian Social Survey (EASS) project. Among the four EASS countries and region, the response pattern of the Japanese is somewhat different from that of Taiwanese, Korean, and Chinese people; the Japanese prefer to give a mid‐point or close‐to‐the‐middle response instead of a definite expression of agreement or disagreement. Social surveys in Japan often handle this tendency by avoiding the use of strong adverbs and excluding mid‐point and off‐scale options from agreement scales. Based on examinations of existing surveys results and conducting pretests, the following strategies to ensure procedural equivalence as well as interpretive equivalence in the EASS project were contrived: (i) The agreement scale was designed to have a sufficient variability in response distributions for all countries and the region; a 7‐point scale with the adverb "strongly" at both ends and a mid‐point was adopted. (ii) The translations of response statements as well as questions for all teams were carefully checked and adjusted through several languages.

This paper identifies subjective well-being trajectories through happiness measures as influenced by time, socio-economic, demographic and behavioural determinants. Hierarchical age-period-cohort models are applied to European Social Survey (2002–2016) data on the population aged 30 and older in 10 countries. A U-shaped relationship between age and happiness is found for some countries, but a rather flat pattern and considerable diversity beyond age 80 are detected for other countries. Lower happiness levels are found for baby boomers (1945–1964) than for preboomers and post-boomers, and also for late boomers (1955–1964) than for early boomers (1945–1954). Women, highly educated and native people are shown to have higher happiness levels than men, less educated and non-native people, respectively. Moreover, a positive assessment of income, having a partner, and being a parent, in good health, employed and socially active are all found to have a positive impact on happiness levels. We find evidence of gaps in happiness levels due to differences in socio-economic characteristics over the life course in some, but not in all of the countries analysed.

Cross-national surveys face the particular challenge of trying to balance optimal survey quality within a
country with comparability across countries in terms of data quality. Addressing this challenge during
fieldwork requires effective management of fieldwork data, a task made more difficult by the inherent
differences between countries. This article argues that changes to the comprehensiveness, timeliness,
consistency, and accessibility of fieldwork data facilitate monitoring and represent a necessary step
forward towards achieving optimal data quality in cross-national surveys. It discusses the approach of the
European Social Survey (ESS) for monitoring fieldwork cross-nationally, and specifically, the
improvements made to central monitoring with the introduction of the Fieldwork Management System
(FMS). Through real-life examples of the use of FMS by the central team, the article reflects and
discusses the impact of the changes to fieldwork monitoring. The ESS experience demonstrates that
there is more than one way to realise the benefits of improved fieldwork data management and provides
insights about the process of improving central monitoring of fieldwork for other cross-national surveys.