# Four Years in Review: Statistical Practices of Likert Scales in Human-Robot Interaction Studies

arXiv: https://arxiv.org/abs/2001.03231 · HTML: https://mlsanity.com/paper/2001.03231/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2020.

## Built on by (most cited first, 12 of 12)

- [MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving](https://mlsanity.com/paper/2301.08595/) (2023)
- [Unified Learning from Demonstrations, Corrections, and Preferences during Physical Human-Robot Interaction](https://mlsanity.com/paper/2207.03395/) (2022)
- [PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection](https://mlsanity.com/paper/2212.04708/) (2022)
- [Assessment and manipulation of latent constructs in pre-trained language models using psychometric scales](https://mlsanity.com/paper/2409.19655/) (2024)
- [Improving Robot-Centric Learning from Demonstration via Personalized Embeddings](https://mlsanity.com/paper/2110.03134/) (2021)
- [Towards Equitable Agile Research and Development of AI and Robotics](https://mlsanity.com/paper/2402.08242/) (2024)
- [Socratic: Enhancing Human Teamwork via AI-enabled Coaching](https://mlsanity.com/paper/2502.17643/) (2025)
- [Development and Evaluation of a Learning-based Model for Real-time Haptic Texture Rendering](https://mlsanity.com/paper/2212.13332/) (2022)
- [Comparing Psychometric and Behavioral Predictors of Compliance During Human-AI Interactions](https://mlsanity.com/paper/2302.01854/) (2023)
- [Demonstration Based Explainable AI for Learning from Demonstration Methods](https://mlsanity.com/paper/2410.05715/) (2024)
- [Towards Formalizing HRI Data Collection Processes](https://mlsanity.com/paper/2203.08396/) (2022)
- [Singing the Body Electric: The Impact of Robot Embodiment on User Expectations](https://mlsanity.com/paper/2401.06977/) (2024)

## Abstract (arXiv)

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.