CQTMS_Hur_2023

122,720 responses from 767 respondents to 160 items.

About this table

DescriptionStrengthening the Reporting of Observational studies in Epidemiology(STROBE)
ReferenceHur, Y., & Seo, D. G. (2023). Development of a character qualities test for medical students in Korea using polytomous item response theory and factor analysis: a preliminary scale development study. Journal of Educational Evaluation for Health Professions, 20. Yera Hur; Dong Gi Seo, 2023, "Development of a character qualities test for medical students in Korea using polytomous item response theory and factor analysis: a preliminary scale development study", https://doi.org/10.7910/DVN/UE55JT, Harvard Dataverse, V1
LicenceCC0 1.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/UE55JT

Size and shape

Responses122,720
Respondents767
Items160
Response categories5
Responses per respondent160
Responses per item767
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational, Targeted/specific
construct typeBehavioral
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)kor
construct nameDevelopment of a character qualities test for medical students in Korea using polytomous item response theory and factor analysis: a preliminary scale development study

Item text

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

InstrumentCharacter Qualities Test for Medical Students (SPHER3C), 160-item preliminary form
Mean words per item4.269
Mean characters per item19.762
Mean characters per response10.200
Flesch-Kincaid grade level2.159

Columns

iditemresp

Get the data

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

Or load it directly in R or Python:

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("CQTMS_Hur_2023")
# Python
pip install irw

import irw
df = irw.fetch("CQTMS_Hur_2023")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse v53.0
Redivis dataset DOI10.57761/4g08-xt41
Manifest pin for this IRW versionv53.0
Metadata sourceirw_meta v23.0