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The Online Laboratory: Conducting Experiments in a Real Labor Market / John J. Horton, David G. Rand, Richard J. Zeckhauser.

NBER Working papers Available online

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Format:
Book
Author/Creator:
Horton, John J.
Contributor:
National Bureau of Economic Research.
Rand, David G.
Zeckhauser, Richard J.
Series:
Working Paper Series (National Bureau of Economic Research) no. w15961.
NBER working paper series no. w15961
Language:
English
Physical Description:
1 online resource: illustrations (black and white);
Other Title:
The Online Laboratory
Place of Publication:
Cambridge, Mass. National Bureau of Economic Research 2010.
Summary:
Online labor markets have great potential as platforms for conducting experiments, as they provide immediate access to a large and diverse subject pool and allow researchers to conduct randomized controlled trials. We argue that online experiments can be just as valid - both internally and externally - as laboratory and field experiments, while requiring far less money and time to design and to conduct. In this paper, we first describe the benefits of conducting experiments in online labor markets; we then use one such market to replicate three classic experiments and confirm their results. We confirm that subjects (1) reverse decisions in response to how a decision-problem is framed, (2) have pro-social preferences (value payoffs to others positively), and (3) respond to priming by altering their choices. We also conduct a labor supply field experiment in which we confirm that workers have upward sloping labor supply curves. In addition to reporting these results, we discuss the unique threats to validity in an online setting and propose methods for coping with these threats. We also discuss the external validity of results from online domains and explain why online results can have external validity equal to or even better than that of traditional methods, depending on the research question. We conclude with our views on the potential role that online experiments can play within the social sciences, and then recommend software development priorities and best practices.
Notes:
Print version record
May 2010.

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