faruk_2026_rpsc

6,854 responses from 298 respondents to 23 items.

About this table

DescriptionRacial Prejudice Scale for Children (23 items, 1-4) from a scale-development study of 298 Bangladeshi children and adolescents aged 11-18.
ReferenceFaruk, M. O. (2026). Racial Prejudice Scale Development Dataset_MOF & MKM [Data set]. figshare. https://doi.org/10.6084/m9.figshare.32939375.v2
LicenceCC BY 4.0
Source datahttps://doi.org/10.6084/m9.figshare.32939375.v2

Size and shape

Responses6,854
Respondents298
Items23
Response categories4
Responses per respondent23
Responses per item298
Density1
LongitudinalFALSE

Classification

age rangeMixed
child age (for child-focused studies)Child (6-12y), Adolescent (12-18y)
sampleEducational, General/non-specific
construct typeOpinion/attitude
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)ben
construct nameRacial prejudice (RPSC)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, faruk_2026_rpsc.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).made in the build script
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Respondent covariates
cov_ageCovariate Age
cov_analysis_sampleCovariate Analysis_Type
cov_family_typeCovariate Nat_F
cov_feeling_thermometerCovariate FT
cov_gradeCovariate Grade
cov_hours_with_friendsCovariate HS_F
cov_languageCovariate Language
cov_religionCovariate Religion
cov_sesCovariate Socio_ES
cov_sexCovariate Sex

What item codes and covariate values stand for is in the source’s codebook, 67070450.

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("faruk_2026_rpsc")
# Python
pip install irw

import irw
df = irw.fetch("faruk_2026_rpsc")

How to cite

@misc{https://doi.org/10.6084/m9.figshare.32939375.v2,
  doi = {10.6084/M9.FIGSHARE.32939375.V2},
  url = {https://figshare.com/articles/dataset/Racial_Prejudice_Scale_Development_Dataset_MOF_MKM/32939375/2},
  author = {Faruk, Md. Omar},
  keywords = {Social psychology, Mental health services},
  title = {Racial Prejudice Scale Development Dataset_MOF &amp; MKM},
  publisher = {figshare},
  year = {2026},
  copyright = {Creative Commons Attribution 4.0 International}
}

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