Over the period 2023-2025 a large consortium of researchers from across Europe worked on a large project ' Smart Survey Implementation', which had the goal of establishing an infrastructure, legal basis, and methodology for doing the European Household Budget (HBS) and Time Use Survey (TUS) in a smart way. In the smart HBS, respondents take pictures of receipts they receive when buying products. In the mart TUS, GPS positioning is used to pre-populate a Time Use diary study. Apps, and microservices that process sensor data ar central to the project. This presentation focuses on learnings from the project when it comes to methodology and data quality. We will report findings from several small-scale and large experiments conducted in Norway, Belgium, Germany, France. the Netherlands and Italy, that focused on 1) the succesful recruitment of respondents into smart surveys 2) the use of machine learning to process sensor data 3) interaction of respondents with (pre-processed sensor data), and 4) measurement effects that occur within smart surveys. We will here concentrate on the effects on data quality. What particular recruitment method for a smart survey is successful in terms of achieving good response rates, and low selection bias? What errors are introduced in processing and aggregating smartphone sensor data, and how can these be decreased. How can respondents help to correct measurement errors, and encouraged to provide high-quality data? In the presentation we will focus on errors of measurement and selection in the survey lifecycle, and conclude by presentation best-practices and open research questions when it comes to the quality of sensor data in smart surveys.
