altinsoy_2024_ol_readiness

17,010 responses from 945 respondents to 18 items.

One of 3 tables from the same source; this one measures Online learning readiness. The others are listed under Related tables.

About this table

DescriptionOnline learning readiness scale (Turkish), 18 items, 1-5; N = 945 university students in a compulsory online foreign-language course; cov_gender, cov_age, cov_school.
ReferenceAltınsoy, E., & Boyraz, S. (2024). The mediating role of engagement in online learning between online learning readiness and online learning satisfaction. Mendeley Data, V1. https://doi.org/10.17632/h8s8vxz3fb
LicenceCC BY 4.0
Source datahttps://data.mendeley.com/datasets/h8s8vxz3fb

Size and shape

Responses17,010
Respondents945
Items18
Response categories5
Responses per respondent18
Responses per item945
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational
construct typeOpinion/attitude
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)tur
construct nameOnline learning readiness

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, altinsoy_2024_online_learning.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 Yasiniz
cov_genderCovariate Cinsiyet
cov_schoolCovariate Okulunuz

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("altinsoy_2024_ol_readiness")
# Python
pip install irw

import irw
df = irw.fetch("altinsoy_2024_ol_readiness")

How to cite

@misc{https://doi.org/10.17632/h8s8vxz3fb,
  doi = {10.17632/H8S8VXZ3FB},
  url = {https://data.mendeley.com/datasets/h8s8vxz3fb},
  author = {Altınsoy, Ertan and BOYRAZ, SERKAN},
  keywords = {University Student},
  title = {The mediating role of engagement in online learning between online learning readiness and online learning satisfaction},
  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