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NCRM: Using smartphone sensors, apps and wearables in Social Science Research

March 19 @ 8:00 am - March 20 @ 5:00 pm

Smartphone sensors (e.g., GPS, camera, accelerometer), apps, and wearables (e.g., smartwatches, fitness bracelets) allow researchers to collect rich behavioral data, potentially with less measurement error and lower respondent burden than self-reports through surveys. Passive mobile data collection (e.g., location tracking, call logs, browsing history) and respondents performing additional tasks on smartphones (e.g., taking pictures, scanning receipts) can augment or replace self-reports in surveys. However, there are multiple challenges to collecting these data: participant selectivity, (non)willingness to provide sensor data or perform additional tasks, privacy concerns and ethical issues, quality and usefulness of the data, and practical issues of implementation. This course will address the challenges by reviewing state-of-the-art practices of smartphone sensor, app, and wearables data collection, ranging from small-scale studies of hard-to-reach populations to large-scale studies to produce official statistics, and discuss design best-practices for this type of measurement. Recommendations provided will include:

•   What research questions can be answered using smartphone sensors, apps, and wearables?

•   What are participants’ concerns and how to address them?

•   How to ask for consent for sensor measurements and ensure participation?

•   How to ease into sensor data analysis?

As part of this course, participants will have the chance to work on practical issues of implementing smartphone sensors, apps, and wearables into social science research. Participants will discuss their own research study designs using new technology and have the opportunity to get hands-on practice with data from health, accelerometery, and location sensors. The course will not discuss how to collect and analyse “found” data (e.g., social media data) nor demonstrate how to program smartphone sensor apps.

The course covers:

  • Contemporary uses of sensor-based data collection in the social sciences
  • Design of surveys with sensor-based data collection components
  • Overview of practical issues when implementing smartphone sensors, apps, and wearables studies and recommendations
  • Introduction to accessing and working with sensor data

By the end of the course participants will:

  • know what smartphone sensors, apps, and wearables are available and what they can measure to facilitate and enhance surveys
  • be able to identify potential applications of sensor and app measurement for their own data collection
  • be able to anticipate practical issues when implementing sensor-based data collection

Participants will perform basic analysis of sensor-collected data using software that they are most comfortable with (SPSS, R, Stata, MS Excel etc.). R syntax will be provided, which can be modified to SPSS or Stata syntax if R is not used. No prior knowledge of smartphone sensors, wearables, and apps is required, but a basic understanding of survey practice and survey errors is helpful. Basic data analysis skills are beneficial.

The course is intended for survey practitioners, researchers, and students who want a practical introduction to smartphone sensors, wearables and app-based research.

Participants should bring their smartphones and laptops and any wearables (e.g., fitness bracelets, smartwatches) if available.

 

The fee per teaching day is:

• £30 per day for UK/EU registered students
• £60 per day for staff at UK/EU academic institutions, UK/EU Research Councils researchers, UK/EU public sector staff and staff at UK/EU registered charity organisations and recognised UK/EU research institutions.
• £220 per day for all other participants

All fees include event materials and refreshments. They do not include lunch, travel and accommodation costs.

Details

Start:
March 19 @ 8:00 am
End:
March 20 @ 5:00 pm
Event Categories:
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Website:
https://www.ncrm.ac.uk/training/show.php?article=10251

Organizer

Old Building, London School of Economics, Houghton Street, London