Going beyond the single item: deriving and evaluating a composite subjective wellbeing measure in the Swiss Household Panel

The Swiss Household Panel (SHP) is an invaluable source of knowledge about wellbeing at the population level and its changes over time in Switzerland, allowing for cross-country comparisons. However, researchers using SHP data have been inconsistent in their choice and use of wellbeing indicators, making comparability of findings across studies difficult. With this guide, our aim was to derive an aggregate measure that maximises the SHP’s potential
to examine multiple dimensions of wellbeing and examine its validity and reliability. This will help researchers to make more informed decisions when using wellbeing measures in the SHP. This study was theoretically guided by the seminal work of Ed Diener on subjective wellbeing. Due to the availability of the measures over time, we focused on affect (emotional measures) and life satisfaction (cognitive measures).
We assessed the factorial structure and internal reliability of the wellbeing indicators available in the SHP and tested their measurement invariance across age groups, periods, gender, questionnaire languages, and survey modes. We demonstrated that combining single items in the SHP can derive a psychometrically robust wellbeing measure.Although an overall score of wellbeing combining all items into one indicator showed satisfactory internal reliability, such a one-dimensional measure should be used with caution, as our findings suggest that wellbeing as it is operationalized using the items available in the SHP is not a unidimensionalconstruct. Instead, we recommend using two subscales that should be analyzed separately: 1) positive affect and life satisfaction, and 2) negative affect. However, caution is needed when age or language groups are compared, as certain items behaved differently across groups. This guide provides a step-by-step approach to developing a wellbeing measure that combines single items from different batteries and rigorously assesses its statistical properties. In this way, it can inform researchers using the SHP data on how to move beyond using separate items to construct a wellbeing measure consisting of two dimensions. Furthermore, bringing more consistency to analyzing wellbeing using the SHP will facilitate comparability and help interpret effect sizes.