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Main Authors: Molan, Jaclyn, Saad, Laura, Roesler, Eileen, McCurry, J. Malcolm, Gyory, Nathaniel, Trafton, J. Gregory
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2501.14099
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author Molan, Jaclyn
Saad, Laura
Roesler, Eileen
McCurry, J. Malcolm
Gyory, Nathaniel
Trafton, J. Gregory
author_facet Molan, Jaclyn
Saad, Laura
Roesler, Eileen
McCurry, J. Malcolm
Gyory, Nathaniel
Trafton, J. Gregory
contents There are currently no psychometrically valid tools to measure the perceived danger of robots. To fill this gap, we provided a definition of perceived danger and developed and validated a 12-item bifactor scale through four studies. An exploratory factor analysis revealed four subdimensions of perceived danger: affective states, physical vulnerability, ominousness, and cognitive readiness. A confirmatory factor analysis confirmed the bifactor model. We then compared the perceived danger scale to the Godspeed perceived safety scale and found that the perceived danger scale is a better predictor of empirical data. We also validated the scale in an in-person setting and found that the perceived danger scale is sensitive to robot speed manipulations, consistent with previous empirical findings. Results across experiments suggest that the perceived danger scale is reliable, valid, and an adequate predictor of both perceived safety and perceived danger in human-robot interaction contexts.
format Preprint
id arxiv_https___arxiv_org_abs_2501_14099
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle The Perceived Danger (PD) Scale: Development and Validation
Molan, Jaclyn
Saad, Laura
Roesler, Eileen
McCurry, J. Malcolm
Gyory, Nathaniel
Trafton, J. Gregory
Robotics
There are currently no psychometrically valid tools to measure the perceived danger of robots. To fill this gap, we provided a definition of perceived danger and developed and validated a 12-item bifactor scale through four studies. An exploratory factor analysis revealed four subdimensions of perceived danger: affective states, physical vulnerability, ominousness, and cognitive readiness. A confirmatory factor analysis confirmed the bifactor model. We then compared the perceived danger scale to the Godspeed perceived safety scale and found that the perceived danger scale is a better predictor of empirical data. We also validated the scale in an in-person setting and found that the perceived danger scale is sensitive to robot speed manipulations, consistent with previous empirical findings. Results across experiments suggest that the perceived danger scale is reliable, valid, and an adequate predictor of both perceived safety and perceived danger in human-robot interaction contexts.
title The Perceived Danger (PD) Scale: Development and Validation
topic Robotics
url https://arxiv.org/abs/2501.14099