  As robots become more prevalent, the importance of the field of human-robot
interaction (HRI) grows accordingly. As such, we should endeavor to employ the
best statistical practices. Likert scales are commonly used metrics in HRI to
measure perceptions and attitudes. Due to misinformation or honest mistakes,
most HRI researchers do not adopt best practices when analyzing Likert data. We
conduct a review of psychometric literature to determine the current standard
for Likert scale design and analysis. Next, we conduct a survey of four years
of the International Conference on Human-Robot Interaction (2016 through 2019)
and report on incorrect statistical practices and design of Likert scales.
During these years, only 3 of the 110 papers applied proper statistical testing
to correctly-designed Likert scales. Our analysis suggests there are areas for
meaningful improvement in the design and testing of Likert scales. Lastly, we
provide recommendations to improve the accuracy of conclusions drawn from
Likert data.

Human-Computer Interaction;Robotics;Applications;Methodology