Tree visualization with tree-maps: 2-d space-filling approach
ACM Transactions on Graphics (TOG)
User Studies: Why, How, and When?
IEEE Computer Graphics and Applications
The Effect of Aesthetic on the Usability of Data Visualization
IV '07 Proceedings of the 11th International Conference Information Visualization
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Shaping of Information by Visual Metaphors
IEEE Transactions on Visualization and Computer Graphics
Perceptual Organization in User-Generated Graph Layouts
IEEE Transactions on Visualization and Computer Graphics
Crowdsourcing for relevance evaluation
ACM SIGIR Forum
How well do line drawings depict shape?
ACM SIGGRAPH 2009 papers
Crowdsourcing graphical perception: using mechanical turk to assess visualization design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Preconceptions and individual differences in understanding visual metaphors
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
The impact of social information on visual judgments
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Patterns for visualization evaluation
Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization
Selecting semantically-resonant colors for data visualization
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
| Hi-index | 0.00 |
Online studies are an attractive alternative to the laborintensive lab study, and promise the possibility of reaching a larger variety and number of people than at a typical university. There are also a number of draw-backs, however, that have made these studies largely impractical so far. Amazon's Mechanical Turk is a web service that facilitates the assignment of small, web-based tasks to a large pool of anonymous workers. We used it to conduct several perception and cognition studies, one of which was identical to a previous study performed in our lab. We report on our experiences and present ways to avoid common problems by taking them into account in the study design, and taking advantage of Mechanical Turk's features.