Conducting interactive experiments online

TLDR

Online labor markets enable behavioral research, yet interactive experiments face methodological challenges, notably participant dropout. The paper discusses how interactive experiments differ between lab and online settings, using a long, complex public goods experiment as a case study. The authors ran a repeated public goods game with and without punishment on both lab participants and Amazon Mechanical Turk, addressing dropout and demonstrating its exogeneity. Cooperation and punishment patterns observed in the lab replicate online, dropout is exogenous, and overall data quality is adequate, supporting online interactive experiments as a reliable complement to lab studies.

Abstract

Online labor markets provide new opportunities for behavioral research, but conducting economic experiments online raises important methodological challenges. This particularly holds for interactive designs. In this paper, we provide a methodological discussion of the similarities and differences between interactive experiments conducted in the laboratory and online. To this end, we conduct a repeated public goods experiment with and without punishment using samples from the laboratory and the online platform Amazon Mechanical Turk. We chose to replicate this experiment because it is long and logistically complex. It therefore provides a good case study for discussing the methodological and practical challenges of online interactive experimentation. We find that basic behavioral patterns of cooperation and punishment in the laboratory are replicable online. The most important challenge of online interactive experiments is participant dropout. We discuss measures for reducing dropout and show that, for our case study, dropouts are exogenous to the experiment. We conclude that data quality for interactive experiments via the Internet is adequate and reliable, making online interactive experimentation a potentially valuable complement to laboratory studies.