le_2024_digicas_hps

12,987 responses from 351 respondents to 37 items.

About this table

DescriptionDigital Competence Assessment Scale for Health Professions Students, 37 items (1 very unlikely - 5 very likely); N = 351 Vietnamese health-professions students; cov_gender, cov_age, cov_year, cov_major, cov_device.
ReferenceLe, C. N., Nguyen, U., & Le, M. L. (2024). Development and validation of digital competence assessment scale for health professions students (DigiCAS-HPS) during post-COVID-19 new normal era [data]. Mendeley Data. https://doi.org/10.17632/z9w44pc4dp
LicenceCC BY 4.0
Source datahttps://data.mendeley.com/datasets/z9w44pc4dp

Size and shape

Responses12,987
Respondents351
Items37
Response categories5
Responses per respondent37
Responses per item351
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational
construct typeCognitive/educational
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct nameDigital competence of health-professions students (DigiCAS-HPS)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, le_2024_digicas_hps.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_deviceCovariate device
cov_genderCovariate sex
cov_majorCovariate major
cov_yearCovariate year

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("le_2024_digicas_hps")
# Python
pip install irw

import irw
df = irw.fetch("le_2024_digicas_hps")

How to cite

@misc{https://doi.org/10.17632/z9w44pc4dp,
  doi = {10.17632/Z9W44PC4DP},
  url = {https://data.mendeley.com/datasets/z9w44pc4dp},
  author = {Le, Cua Ngoc},
  keywords = {Digital Education},
  title = {Development and validation of digital competence assessment scale for health professions students (DigiCAS-HPS) during post- COVID-19 new normal era},
  publisher = {Mendeley Data},
  year = {2024}
}

Version and provenance

IRW versionv584
Redivis datasetitem_response_warehouse_7 v1.1
Redivis dataset DOI10.57761/7pd3-sh31
Manifest pin for this IRW versionv1.0
Metadata sourceirw_meta v44.0