A Methodology for Testing Voting Systems

Peer-reviewed Article

pp. 7-21Download full article (PDF)


This paper compares the relative merit in realistic versus lab style experiments for testing voting technology. By analyzing three voting experiments, we describe the value of realistic settings in showing the enormous challenges for voting process control and consistent voting experiences.

The methodology developed for this type of experiment will help other researchers to test polling place protocols and administration. Comparing the results from laboratory experiments with voter verification and realistic voting experiments further validates the procedure of testing equipment in laboratory settings.

The methodology and protocol for testing voting systems can be applied to any voting technology. This protocol matches the real-world conditions of voting by replicating them for the experiment.

Practitioner’s Take Away

  • Voting systems present high stakes technology whose criteria for success depends on usability, security, and reliability. This type of system benefits from testing in real-world conditions to gain better understanding of the issues.
  • The best practice protocol for testing voting systems in real-world conditions include simulation of polling place, voting systems, recognizable candidates, and real poll workers.
  • Voting experiments that use regular polling places as the test venue risk complicating the resulting data. To reduce complications, on-site training must be done in advance. Furthermore, voting systems must also be thoroughly tested for quality assurance in the voting test environment, not just in the lab.
  • Ballot design must be checked carefully to avoid confusion to voters in experimental mock elections. This includes clear instructions and discussion with participants regarding candidate choices.
  • Experimental protocols using regular poll workers must include clear training, instructions, and scripts. Studies showed that regular poll workers can add a level of confusion and add to lack of control with data sets if they are not trained correctly.