The potential of survey and questionnaire design to achieve measurement invariance 2 |
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| Coordinator 1 | Dr Katharina Meitinger (Utrecht University) |
| Coordinator 2 | Professor Natalja Menold (TU Dresden) |
| Coordinator 3 | Dr Heinz Leitgöb (Leipzig University) |
A common finding in measurement invariance testing is that the property of metric or scalar measurement invariance is difficult to achieve in cross-cultural survey data. Whereas approximate approaches of measurement invariance testing received great interest, the impact of survey methodological decisions on the results of measurement invariance analysis have been relatively underemphasized. However, previous research revealed the serious impact of various survey methodological aspects on measurement invariance, such as differences in question wording, translations, rating scale forms, visual presentation, modes, or devices. At the same time, survey methodology also provides us with a toolkit to improve the measurement invariance of survey questions. Optimal translation procedures (e.g., TRAPD approach) or approaches at the development and pretesting stage (e.g., focus groups, expert reviews, cross-cultural cognitive interviewing, web probing) can potentially improve the comparability of survey items. Some of these approaches could also be implemented during or after the actual data collection (e.g., web probing). Careful conceptualization and operationalization can help to improve the factorial structure of indicators and therefore reveal more promising measurement invariance results. Anchoring vignettes or similar approaches to control for differential item functioning could help to adjust data and to improve their comparability, which should also improve the results of measurement invariance analysis.
This session wants to provide a platform for survey methodological evidence to improve measurement comparability. The aim is to foster a discussion on survey methodological approaches to improve data comparability evaluated by measurement invariance analysis before, during, or after the data has been collected.