Demo Data from EDBT 2026 / reviewers in the wild / expert
Elizabeth K. Phillips
dblp:371/3073
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0001-9511-3275ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Design research and methods · 77% Usability and user experience research · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Design research and methods
research methodology |
0.9 | 1 | 2025 | A Tutorial for Finding and Evaluating HRI Scales · HRI 2025 |
Usability and user experience research
psychometric evaluation |
0.3 | 1 | 2025 | A Tutorial for Finding and Evaluating HRI Scales · HRI 2025 |
Methods — techniques the papers use, named apart from their topics
scale validation · 0.9psychometric theory · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Choosing the "Perfect" Scale: A Primer to Evaluate Existing Scales in HRIabstractScales are commonly employed in Human–Robot Interaction (HRI) research, yet due to its multidisciplinary nature, many in this community lack direct training in psychometrics. This poses challenges for appropriate scale selection, accurate assessments of reliability and validity, and use. We provide a tutorial to empower researchers without scale development expertise to assess scale quality efficiently. We detail a guideline that provides high-level questions and examples to help the reader make confident evaluations of existing scales in HRI. The guideline is then used to evaluate the Godspeed and Robotic Social Attributes Scale (RoSAS). RoSAS is found to be adequately validated, whereas Godspeed warrants further investigation before it should be used in HRI contexts. The article concludes by offering advice on the use of custom scales and provides references for further enhancing expertise in this domain. Laura Saad, Eileen Roesler, Elizabeth K. Phillips, J. Gregory Trafton |
ACM Trans. Hum. Robot Interact. | 3 |
| 2025 | A Tutorial for Finding and Evaluating HRI ScalesabstractConstruct measurement scales are commonly employed in HRI research. We provide a half-day tutorial (4 hours) that aims to empower researchers with the tools to find appropriate scales for their research and assess the quality of those scales confidently and efficiently. There are no prerequisites required for attendees. We aim to recruit researchers interested in using scales but who lack confidence in evaluating their development. The first part of the tutorial will teach attendees how to assess the quality of HRI scales. To accomplish this, we will review basic topics in psychometric theory and a guideline (developed by the organizers) that outlines best practices in scale development and validation. In the second part, we will apply this guideline to two frequently used HRI scales: Godspeed and RoSAS. Attendees are also encouraged to bring scales they are interested in reviewing. The third part aims to help attendees find appropriate scales for their research. To accomplish this, we will debut a new HRI scale database we have developed. This database is the first centralized online repository of HRI scales and contains over 40 of the most used and cited HRI scales covering a wide array of topics of interest such as, trust, embodiment, safety, and attitudes towards robots. We will demonstrate how to access and use the information contained within the database. Our goal for this tutorial is to promote active engagement from attendees throughout the session, ultimately striving to improve the quality and replicability of results in HRI studies. Laura Saad, Eileen Roesler, Elizabeth K. Phillips, J. Gregory Trafton |
HRI | 3 |