martinez_lopez_2019_cobadi

6,970 responses from 205 respondents to 34 items.

About this table

Description34 COBADI self-rated digital-competence items (1-4) in four blocks (q26 13 items, q27 12, q28 5, q29 4); N = 205 students of a Russian university; cov_gender, cov_age.
ReferenceMartinez-Lopez, R. (2019). Dataset of the type of the Internet use and web 2.0 digital competence of students in a Russian University [data]. Mendeley Data. https://doi.org/10.17632/x4r7hpn4cz
LicenceCC BY 4.0
Source datahttps://data.mendeley.com/datasets/x4r7hpn4cz

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

Responses6,970
Respondents205
Items34
Response categories4
Responses per respondent34
Responses per item205
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational
construct typeCognitive/educational
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)rus
construct nameCOBADI web 2.0 basic digital competence, Russian version (34 items, 4 blocks)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, martinez_lopez_2019_cobadi.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 made in the build script
cov_genderCovariate made in the build script

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("martinez_lopez_2019_cobadi")
# Python
pip install irw

import irw
df = irw.fetch("martinez_lopez_2019_cobadi")

How to cite

@misc{https://doi.org/10.17632/x4r7hpn4cz,
  doi = {10.17632/X4R7HPN4CZ},
  url = {https://data.mendeley.com/datasets/x4r7hpn4cz},
  author = {Martinez-Lopez, Ruth},
  keywords = {Education, Internet, Questionnaires, Higher Education, Russia, Digital Technology, Postgraduate Education, Self-Efficacy, Student, Tool Use, Media Use},
  title = {Dataset of the type of the Internet use and web 2.0 digital competence of students in a Russian University},
  publisher = {Mendeley},
  year = {2019}
}

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