kermen_2019_self_regulation

5,315 responses from 334 respondents to 16 items.

One of 4 tables from the same source; this one measures Perceived Self-Regulation Scale. The others are listed under Related tables.

About this table

DescriptionPerceived Self-Regulation Scale (16 items), 1-5, 334 high-school students in Istanbul, Turkey; cov_gender (1 male, 2 female), cov_grade, cov_age.
ReferenceKermen, U. (2019). Anxiety, self-efficacy, and self-regulation in high school students: The mediating role of attentional control [Data set]. Dryad. https://doi.org/10.5061/dryad.vhhmgqnpv
LicenceCC0 1.0
Source datahttps://doi.org/10.5061/dryad.vhhmgqnpv

Size and shape

Responses5,315
Respondents334
Items16
Response categories5
Responses per respondent15.913
Responses per item332.188
Density0.995
LongitudinalFALSE

Classification

age rangeMixed
child age (for child-focused studies)Adolescent (12-18y)
sampleEducational
construct typePersonality
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)tur
construct namePerceived Self-Regulation Scale

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, kermen_2019_attentional_control.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).sira
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Respondent covariates
cov_ageCovariate yas
cov_genderCovariate cinsiyet
cov_gradeCovariate sinif

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("kermen_2019_self_regulation")
# Python
pip install irw

import irw
df = irw.fetch("kermen_2019_self_regulation")

How to cite

@misc{https://doi.org/10.5061/dryad.vhhmgqnpv,
  doi = {10.5061/DRYAD.VHHMGQNPV},
  url = {https://datadryad.org/dataset/doi:10.5061/dryad.vhhmgqnpv},
  author = {Kermen, Umut},
  language = {en},
  title = {Anxiety, self-efficacy, and self-regulation in high school students The mediating role of attentional control},
  publisher = {Dryad},
  year = {2019},
  copyright = {Creative Commons Zero v1.0 Universal}
}

Version and provenance

IRW versionv584
Redivis datasetitem_response_warehouse_5 v8.5
Redivis dataset DOI10.57761/41xy-5k56
Manifest pin for this IRW versionv8.5
Metadata sourceirw_meta v44.0