Evidence map›Paper›PMID 35155588›Full record

ArticleFrontiers in robotics and AI2021

Lessons Learned About Designing and Conducting Studies From HRI Experts.

Marlena R Fraune, Iolanda Leite, Nihan Karatas, Aida Amirova, Amélie Legeleux, Anara Sandygulova, Anouk Neerincx, Gaurav Dilip Tikas, Hatice Gunes, Mayumi Mohan and 5 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
4.7field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 12 institutions in 8 countries.

Marlena R FrauneIntergroup Human-Robot Interaction (iHRI) Lab, Department of Psychology, New Mexico State University, Las Cruces, NM, United States.
Iolanda LeiteDivision of Robotics, Perception, and Learning (RPL), School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Nihan KaratasHuman-Machine Interaction (HMI) and Human Characteristics Research Division, Institutes of Innovation for Future Society, Nagoya University, Nagoya, Japan.
Aida AmirovaDepartment of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan.
Amélie LegeleuxLab-STICC, University of South Brittany, CNRS UMR 6285, Brest, France.
Anara SandygulovaDepartment of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan.
Anouk NeerincxLab-STICC, University of South Brittany, CNRS UMR 6285, Brest, France.
Gaurav Dilip TikasStrategy, Innovation and Entrepreneurship Area, Institute of Management Technology, Ghaziabad, India.
Hatice GunesAffective Intelligence and Robotics Lab, Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Mayumi MohanHaptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany.
Nida Itrat AbbasiAffective Intelligence and Robotics Lab, Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Sudhir ShenoyHuman-AI Technology Lab, Computer Engineering Program, University of Virginia, Charlottesville, VA, United States.
Brian ScassellatiSocial Robotics Lab, Department of Computer Science, Yale University, New Haven, CT, United States.
Ewart J de VisserWarfighter Effectiveness Research Center, U.S. Air Force Academy, Colorado Springs, CO, United States.
Takanori KomatsuDepartment of Frontier Media Science, School of Interdisciplinary Mathematical Science, Meiji University, Tokyo, Japan.
Centre National de la Recherche Scientifique · FRNazarbayev University · KZUniversity of Cambridge · GBInstitute of Management Technology · INKTH Royal Institute of Technology · SEMax Planck Institute for Intelligent Systems · DEMeiji University · JPNagoya University · JPNew Mexico State University · USUnited States Air Force Academy · USUniversity of Virginia · USYale University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI. In this workshop, we grouped participants with mentors, who are experts in areas like real-world studies, empirical lab studies, questionnaire design, interview, participatory design, and statistics. During and after the workshop, participants discussed their proposed study methods, obtained feedback, and improved their work accordingly. In this paper, we present 1) Workshop attendees' feedback about the workshop and 2) Lessons that the participants learned during their discussions with mentors. Participants' responses about the workshop were positive, and future scholars who wish to run such a workshop can consider implementing their suggestions. The main contribution of this paper is the lessons learned section, where the workshop participants contributed to forming this section based on what participants discovered during the workshop. We organize lessons learned into themes of 1) Improving study design for HRI, 2) How to work with participants - especially children -, 3) Making the most of the study and robot's limitations, and 4) How to collaborate well across fields as they were the areas of the papers submitted to the workshop. These themes include practical tips and guidelines to assist researchers to learn about fields of HRI research with which they have limited experience. We include specific examples, and researchers can adapt the tips and guidelines to their own areas to avoid some common mistakes and pitfalls in their research.

Indexed as

human-robot interactionmethodologyqualitativequantitativereplicationreproducibilityresearchstatistics

Identifiers

PMID35155588
PMCPMC8832512
OpenAlexW4210718321

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.