Cookies?
Library Header Image
LSE Research Online LSE Library Services

Sounds of silence: data for analysing muted safety voice in speech

Noort, Mark C., Reader, Tom W. and Gillespie, Alex ORCID: 0000-0002-0162-1269 (2021) Sounds of silence: data for analysing muted safety voice in speech. Data In Brief, 37. ISSN 2352-3409

[img] Text (1-s2.0-S2352340921004704-main) - Published Version
Available under License Creative Commons Attribution.

Download (253kB)

Identification Number: 10.1016/j.dib.2021.107186

Abstract

Transcribed text from simulated hazards contains important content relevant for preventing harm. By capturing and analysing the content of speech when people raise (safety voice) or withhold safety concerns (safety silence), communication patterns may be identified for when individuals perceive risk, and safety management may be improved through identifying potential antecedents. This dataset contains transcribed speech from 404 participants (n students = 377; n female = 277, Age M (sd) = 22.897 (5.386)) engaged in a simulated hazardous scenario (walking across an unsafe plank), capturing 18,078 English words (M (sd) = 46.117 (37.559)). The data was collected through the Walking the plank paradigm (Noort et al, 2019), which provides a validated laboratory experiment designed for the direct observation of communication in response to hazardous scenarios that elicit safety concerns. Three manipulations were included in the design: hazard salience (salient vs not salient), responsibilities (clear vs diffuse) and encouragements (encouraged vs discouraged). Speech between two set timepoints in the hazardous scenario was transcribed based on video recordings and coded in terms of the extent to which speech involved safety voice or safety silence. Files contain i) a.csv containing the raw data, ii) a.csv providing variable description, iii) a Jupyter notebook (v. 3.7) providing the statistical code for the accompanying research article, iv) a.html version of the Jupyter notebook, v) a.html file providing the graph for the.html Jupyter notebook, vi) speech dictionaries, and vii) a copy of the electronic questionnaire. The data and supplemental files enable future research through providing a dataset in which participants can be distinguished in terms of the extent to which they are concerned and raise or withhold this. It enables speech and conversation analyses and the Jupyter notebook may be adapted to enable the parsing and coding of text using provided, existing and custom dictionaries. This may lead to the identification of communication patterns and potential interventions for unmuting safety voice. This data-in-brief is published alongside the research article: M. C. Noort, T.W. Reader, A. Gillespie. (2021). The sounds of safety silence: Interventions and temporal patterns unmute unique safety voice content in speech. Safety Science.

Item Type: Article
Official URL: https://www.sciencedirect.com/journal/data-in-brie...
Additional Information: © 2021 The Authors
Divisions: Psychological and Behavioural Science
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Date Deposited: 01 Jun 2021 14:39
Last Modified: 20 Jul 2021 00:33
URI: http://eprints.lse.ac.uk/id/eprint/110740

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics