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Mobile Device Sensor Data in ESS Surveys: Utility, Criteria, and Combinations

This workshop explores the utility of mobile device and wearable sensors in European Social Survey (ESS) surveys. It discusses criteria for selecting survey topics and sensor data, identifies potential combinations, and evaluates their effectiveness. The workshop also considers the perspectives of survey respondents and the quality and costs associated with sensor data. Conclusions and future directions are presented.

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Mobile Device Sensor Data in ESS Surveys: Utility, Criteria, and Combinations

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  1. ROME April 11th | 12th 2019 MIMOD Mixed-Mode Designs for Social Surveys FINAL WORKSHOP Mobile device sensor data in ESS surveys WP5 Ole Mussmann and Barry Schouten Istatistics Netherlands (CBS)

  2. Utility of mobile device and wearable sensors • Approach sensor utility: • Construct criteria for potential pairs of ESS surveys and sensor data; • From perspective survey measurement; • From perspective sensor quality and costs; • From perspective respondent; • Identify ESS topics that are candidates under survey measurement criteria; • Make an inventory of mobile device and wearable sensors; • Construct combinations of survey topics and sensors; • Evaluate combinations; • Suggest further exploration; MIMOD project - Mixed-Mode Designs in Social Surveys Rome, 11-12 April 2019

  3. Criteria – survey perspective • Survey perspective • Burden: The survey topic(s) are burdensome for a respondent (time or cognitive effort); • Centrality: The survey topic(s) are non-central to respondents; • Non-survey type: The survey topic(s) do not lend themselves to a survey question-answer approach to begin with; MIMOD project - Mixed-Mode Designs in Social Surveys Rome, 11-12 April 2019

  4. Criteria – sensor perspective and respondent perspective • Sensor perspective • Omnipresence: The sensor(s) are available in most, if not all, contemporary devices; • Data access: Data generated by the sensor(s), as well as metadata about the properties and accuracy of the sensor data, can be accessed and processed; • Quality: The sensor data is comparable, reproducible and accurate; • Costs: Any costs associated with the sensor(s) are affordable in most surveys; • Respondent perspective • Respondent willingness: Respondents are willing to consent to provide the sensor data; • Data handling: Respondents can retrieve, revise and delete sensor data on demand; • Burden: Respondents are willing to devote the effort needed to collect and handle the sensor data; • Feedback: Respondents may retrieve useful knowledge about themselves; MIMOD project - Mixed-Mode Designs in Social Surveys Rome, 11-12 April 2019

  5. Potential survey – sensor combinations Sensor measurements are initiated by respondent Sensor measurements exist as external (big) data MIMOD project - Mixed-Mode Designs in Social Surveys Rome, 11-12 April 2019

  6. Sensor criteria for self-initiated measurements MIMOD project - Mixed-Mode Designs in Social Surveys Rome, 11-12 April 2019

  7. Respondent criteria for self-initiated measurements MIMOD project - Mixed-Mode Designs in Social Surveys Rome, 11-12 April 2019

  8. Sensor criteria for existing measurements MIMOD project - Mixed-Mode Designs in Social Surveys Rome, 11-12 April 2019

  9. Respondent criteria for existing measurements MIMOD project - Mixed-Mode Designs in Social Surveys Rome, 11-12 April 2019

  10. Conclusions & future • Conclusions: • For most of the ESS surveys, there are promising survey-sensor combinations; • Literature and application in official statistics still very thin; • Sensor and respondent criteria can be hard to assess without further empirical evidence; • Future: • Extend and refine assessment of criteria; • Test and pilot most promising options (academic research already on-going); • Coordinate efforts with ESSnet Big Data 2; MIMOD project - Mixed-Mode Designs in Social Surveys Rome, 11-12 April 2019

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