ding_2020_covid_risk_perception

5,844 responses from 1,461 respondents to 4 items.

One of 2 tables from the same source; this one measures COVID-19 risk perception (study-specific). The others are listed under Related tables.

About this table

DescriptionCOVID-19 risk perception items from the authors' own survey, 4 items, 1-5, 1,461 Chinese college students surveyed online 4-7 Feb 2020 during quarantine; cov_gender, cov_age, cov_grade, cov_major, cov_parent_health, cov_known_case, cov_known_contact, cov_school_hubei, cov_home_hubei.
ReferenceDing, Y., Du, X., Li, Q., Zhang, M., Zhang, Q., Tan, X., & Liu, Q. (2020). Risk perception of coronavirus disease 2019 (COVID-19) and its related factors among college students in China during quarantine. PLOS ONE, 15(8), e0237626.
DOI10.1371/journal.pone.0237626
LicenceCC BY 4.0
Source datahttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237626

Notes

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Responses5,844
Respondents1,461
Items4
Response categories5
Responses per respondent4
Responses per item1,461
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational, Internet-based
construct typeOpinion/attitude
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)chi
construct nameCOVID-19 risk perception (study-specific)

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

InstrumentCOVID-19 risk perception items (Part four) of the authors' cognition-attitude survey of Chinese college students, Ding et al. (2020)
Mean words per item11.250
Mean characters per item60.750
Mean characters per response3.850
Flesch-Kincaid grade level7.490

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, ding_2020_covid_students.py. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.

ColumnWhat it isSource column
IRW standard columns
idIRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).ID
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Respondent covariates
cov_ageCovariate Q2
cov_genderCovariate Q1
cov_gradeCovariate Q5
cov_home_hubeiCovariate made in the build script
cov_known_caseCovariate made in the build script
cov_known_contactCovariate made in the build script
cov_majorCovariate Q6
cov_parent_healthCovariate Q9
cov_school_hubeiCovariate made in the build script

What item codes and covariate values stand for is in the source’s codebook, file.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("ding_2020_covid_risk_perception")
# Python
pip install irw

import irw
df = irw.fetch("ding_2020_covid_risk_perception")

How to cite

@article{Ding_2020, title={Risk perception of coronavirus disease 2019 (COVID-19) and its related factors among college students in China during quarantine}, volume={15}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0237626}, DOI={10.1371/journal.pone.0237626}, number={8}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Ding, Yani and Du, Xueying and Li, Qinmei and Zhang, Miao and Zhang, Qingjun and Tan, Xiaodong and Liu, Qing}, editor={Yi, Siyan}, year={2020}, month=Aug, pages={e0237626} }

Version and provenance

IRW versionv584
Redivis datasetitem_response_warehouse_5 v8.5
Redivis dataset DOI10.57761/41xy-5k56
Manifest pin for this IRW versionv8.5
Metadata sourceirw_meta v44.0