Logging the Search Self-Efficacy of Amazon Mechanical Turkers

Abstract

Conducting focused but large-scale studies and experiments of user search behavior is highly desirable. Crowd-sourcing services such as the Amazon Mechanical Turk allow such studies to be conducted quickly and cheaply. They also have the potential to mitigate the problems associated with traditional experimental methods, in particular the relatively small and homogenous participant samples used in typical experiments. Our current research project addresses the relationship between searcher self-ecacy assessments and their strategies for conducting complex searches. In this work-in-progress paper, we describe our initial tests of using Amazon Mechanical Turk to conduct experiments in this area. We describe a platform for logging the actions taken by Turkers, and a questionnaire we conducted to assess search self-ecacy of average Turkers. Our results indicate Turkers have a similar range of search self-ecacy