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Survey Experiments with Google Consumer Surveys: Promise and Pitfalls for Academic Research in Social Science

  • Lie Philip Santoso (a1), Robert Stein (a2) and Randy Stevenson (a3)

In this article, we evaluate the usefulness of Google Consumer Surveys (GCS) as a low-cost tool for doing rigorous social scientific work. We find that its relative strengths and weaknesses make it most useful to researchers who attempt to identify causality through randomization to treatment groups rather than selection on observables. This finding stems, in part, from the fact that the real cost advantage of GCS over other alternatives is limited to short surveys with a small number of questions. Based on our replication of four canonical social scientific experiments and one study of treatment heterogeneity, we find that the platform can be used effectively to achieve balance across treatment groups, explore treatment heterogeneity, include manipulation checks, and that the provided inferred demographics may be sufficiently sound for weighting and explorations of heterogeneity. Crucially, we successfully managed to replicate the usual directional finding in each experiment. Overall, GCS is likely to be a useful platform for survey experimentalists.

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Authors’ note: Replication code and data are available at the Political Analysis Dataverse (Santoso, Stein, and Stevenson 2016) while the Supplementary materials for this article are available on the Political Analysis Web site. We would also like to thank Google Inc. for allowing us to ask some of the questions reported here free of charge.

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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
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