- Surveys have changed significantly since their golden age from 1960-1975 due to factors like increased costs, nonresponse rates, and new technologies. - Users now demand more timely, rich data while balancing reduced respondent burden. This has led to increased use of alternative data sources like administrative records and sensors as well as nonprobability sampling. - Maintaining survey quality is an ongoing challenge due to higher nonresponse, costs, and assumptions like missing at random being unrealistic. Integrating different data sources and new methods is needed to meet demands for wider, deeper, better, quicker, and cheaper data.

1. A Changing Survey
Landscape
Lars Lyberg, Inizio
Presentation at Frimis, November 28, 2018

2. The Golden Age of Survey Research
1960-1975
• Probability sampling well established in most countries (Neyman 1934)
• Nonresponse rate above 10-15% suggested that the survey was bad
• Cost situation was reasonable
• A limited number of modes available
• Things were allowed to take time
• Some error sources were unknown

3. Drivers of Major Developments
Since 1975
• More advanced computers and software
• More sophisticated users
• New modes such as CATI and Voice Recognition
• Increasing nonresponse
• Increasing costs
• The cognitive revolution (CASM)
• Model-assisted surveys
• The quality movement

4. A changing survey landscape
• Users want more timely and richer data
• Increased nonresponse and costs in surveys
• Demands for reduced respondent burden
• New data sources, new actors and new technology
• Combining different data sources
• Nonprobability samples and inferential issues
• New data collection modes and mixed modes
• New storage requirements

5. Examples of Sources of Data
• Censuses
• Other survey programs
• Administrative data systems
• Medical records systems
• Commercially compiled data
• Financial data
• Satellite imagery
• GPS and GIS
Adapted from Heeringa 2018
• Social media
• Mobile devices
• Wearable measurement devices
• Sensors (Internet of Things)
• Visual data: pictures and video
• Genetic profile data
• Transactional data systems

6. Nonprobability Sampling
• Relatively common since 1934
• Rare and hard to sample populations
• Opt-in panels
• Combinations of probability and nonprobability sampling
• Pretests and pilot surveys
• Conferences/workshops

7. Until the purpose is stated, there is no right or
wrong way of going about the survey
Deming, W. E. (1944)

8. User Demands
• More timely data
• Not just estimates
• Advanced products such as interactive features
• Surveys not great for complex concepts (religion, loneliness, happiness,
existential issues, health, hypothetical questions)

9. Quality
• Nonresponse is up
• Costs are up
• Face-to-face almost gone
• Margin of error understated
• Missing at random not a very realistic assumption
• In Sweden only SCB is ISO 20252 certified
• New version of ISO released soon with access panels included
• New ideas involving responsive design have had limited success
• Quality management is struggling
• Lots of bad surveys out there

10. Five formidables challenges for official
statistics: wider, deeper, better, quicker,
cheaper.
Holt, D. 2007
We need to move from a probability sampling
paradigm to a multiple data source paradigm
and then move backwards from concepts to
best data sources.
Citro, C. 2014