The emergence of online labor markets makes it far easier to use individual human raters to evaluate materials for data collection and analysis in the social sciences. In this paper, we report the results of an experiment -conducted in an online labor market -that measured the effectiveness of a collection of social and financial incentive schemes for motivating workers to conduct a qualitative, content analysis task. Overall, workers performed better than chance, but results varied considerably depending on task difficulty. We find that treatment conditions which asked workers to prospectively think about the responses of their peers -when combined with financial incentives -produced more accurate performance. Other treatments generally had weak effects on quality. Workers in India performed significantly worse than US workers, regardless of treatment group. We compare incentive schemes in an online labor market experiment and find that asking subjects to consider the answers of their peers produces more accurate performance on a content analysis task. Workers in India and workers with lower web-browsing skills also performed worse than their peers on the task.
In REF , Shaw et al. conducted an experiment to compare the effectiveness of a collection of social and financial incentive mechanisms.
Designing incentives for inexpert human raters
CSCW '11
Computer Science