li_2025_pprds

5,380 responses from 269 respondents to 20 items.

One of 4 tables from the same source; this one is the pprds table. The others are listed under Related tables.

About this table

DescriptionPeer Pressure on Risky Driving Scale, Chinese version (20 items, 1-5; risk-encouraging, risk-discouraging and indirect peer pressure), from 269 young Chinese drivers
ReferenceLi, W., Liu, J., Wang, Y., & Sun, L. (2025). Psychometric adaptation and validity of the Resistance to Peer Influence Scale among young Chinese drivers and its links with peer pressure and risky driving behaviours. Behavioral Sciences, 15(9), 1237.
DOI10.3390/bs15091237
LicenceCC BY 4.0
Source datahttps://doi.org/10.6084/m9.figshare.29815862

Size and shape

Responses5,380
Respondents269
Items20
Response categories5
Responses per respondent20
Responses per item269
Density1
LongitudinalFALSE

Classification

sampleGeneral/non-specific
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)chi

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, li_2025_peer_driving.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_accidentsCovariate accident
cov_ageCovariate age
cov_drives_with_friendsCovariate friends
cov_driving_freqCovariate Frequency
cov_driving_yearsCovariate Experience
cov_educationCovariate education
cov_penaltiesCovariate penalty
cov_sexCovariate sex

What item codes and covariate values stand for is in the codebook released with the source data.

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("li_2025_pprds")
# Python
pip install irw

import irw
df = irw.fetch("li_2025_pprds")

How to cite

@article{Li_2025, title={Psychometric Adaptation and Validity of the Resistance to Peer Influence Scale Among Young Chinese Drivers and Its Links with Peer Pressure and Risky Driving Behaviours}, volume={15}, ISSN={2076-328X}, url={http://dx.doi.org/10.3390/bs15091237}, DOI={10.3390/bs15091237}, number={9}, journal={Behavioral Sciences}, publisher={MDPI AG}, author={Li, Wenchengxu and Liu, Jiahong and Wang, Yuxi and Sun, Long}, year={2025}, month=Sept, pages={1237} }

Version and provenance

IRW versionv584
Redivis datasetitem_response_warehouse_6 v5.11
Redivis dataset DOI10.57761/qahq-j855
Manifest pin for this IRW versionv5.11
Metadata sourceirw_meta v44.0