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Different factors are known to affect the comparability of multinational, multicultural and multiregional ('3MC') survey data. These include factors relevant to the design of the questionnaire in different contexts (such as cultural differences in how a concept is understood, inaccurate or approximate translations of concepts, and variant adaptations to question formats). Others include factors relating to the survey design in general and how it is implemented across contexts (such as sample design, choice of mode(s), and contact strategies). Together, they contribute item, method and construct biases that can affect the invariance of composite measures. While research to date has looked at the effects of these factors on measurement invariance individually, there have been few attempts to compare them directly and assess their relative impact. To illustrate how this can be done, the present paper tests for measurement invariance in a subjective wellbeing measure across question formats, modes, languages, and countries, combining European Social Survey data from designed and natural experiments (resulting from the use of variant question formulations and translations) from Germany, Switzerland and France. Overall, we find translation errors, language and culture to be bigger sources of non-equivalence than question format and mode. The findings have implications for both survey designers making decisions about optimal resource allocation in the design of 3MC studies, as well as for comparative analysts interested in comparing countries with shared languages and interpreting cross-group differences.

"The pursuit of high response rates to minimise the threat of nonresponse bias continues to dominate decisions about resource allocation in survey research. Yet a growing body of research has begun to question this practice. In this study, we use previously unavailable data from a new sampling frame based on population registers to assess the value of different methods designed to increase response rates on the European Social Survey in Switzerland. Using sampling data provides information about both respondents and nonrespondents, making it possible to examine how changes in response rates resulting from the use of different fieldwork methods relate to changes in the composition and representativeness of the responding sample. We compute an R-indicator to assess representativity with respect to the sampling register variables, and find little improvement in the sample composition as response rates increase. We then examine the impact of response rate increases on the risk of nonresponse bias based on Maximal Absolute Bias (MAB), and coefficients of variation between subgroup response rates, alongside the associated costs of different types of fieldwork effort. The results show that increases in response rate help to reduce MAB, while only small but important improvements to sample representativity are gained by varying the type of effort. These findings lend further support to research that has called into question the value of extensive investment in procedures aimed at reaching response rate targets and the need for more tailored fieldwork strategies aimed both at reducing survey costs and minimising the risk of bias." (author's abstract)