Chapter 26 Surveys

SICSS Summer 2020 -Survey Research in the Digital Age by professor Matthew Salganik
Sampling Interviews Data environment
1st era area probability face-to-face stand-alone
2nd ear random digital dial probability telephone stand-alone
3rd era non-probability computer-administered linked

Total survey error framework (Groves and Lyberg 2010)

Insight:

  1. Errors can come from bias or variance
  2. Total survey error = Measurement error + representation error

(Groves and Lyberg 2010, Fig. 3)


Probability and Non-probability Sampling

  • Probability sample: every unit from a frame population has a known and non-zero probability of inclusion
  • With weighting, we can recover bias in your sampling.
  • Non-response problem

Horvitz-Thompson estimator (or bias estimator):

\[ \hat{\bar{y}} = \frac{\sum_{i \in s}y_i / \pi_i}{N} \]

where \(\pi_i\) = person i’s probability of inclusion (we have to estimate)

Wiki Survey

  • Create a survey that leverages the power of people

Mass Collaboration

  • Human Computation: Train People -> Train Lots of People -> Train Machine

    • Cleaning

    • De-biasing

    • Combining

  • Open Call:

    • solutions are easier to check than generate

    • required specialized skills

  • Distributed Data Collection:

    • People go out and collect data

    • quality check

Fragile family challenges

References

Groves, R. M., and L. Lyberg. 2010. “Total Survey Error: Past, Present, and Future.” Public Opinion Quarterly 74 (5): 849–79. https://doi.org/10.1093/poq/nfq065.