habibi_2021_pe_tsc

1,205 responses from 241 respondents to 5 items.

One of 3 tables from the same source; this one measures Teacher self-concept during the Covid-19 pandemic (TSC, 5 items). The others are listed under Related tables.

About this table

Description5 teacher self-concept items (1-5); N = 241 Indonesian physical-education teachers during COVID-19 distance learning; cov_gender, cov_teaching_level, cov_school_location, cov_school_type.
ReferenceHabibi, A. (2021). Data on Physical Education Teachers' Burnout during Pandemic: Structural Equation Modelling [data]. Mendeley Data. https://doi.org/10.17632/b6zh5mjd5p
LicenceCC BY 4.0
Source datahttps://data.mendeley.com/datasets/b6zh5mjd5p

Size and shape

Responses1,205
Respondents241
Items5
Response categories5
Responses per respondent5
Responses per item241
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleWorkplace
construct typeOpinion/attitude
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)ind
construct nameTeacher self-concept during the Covid-19 pandemic (TSC, 5 items)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard. 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).not traced
itemIRW standard Item identifier.not traced
respIRW standard Response value.not traced
Respondent covariates
cov_genderCovariate not traced
cov_school_locationCovariate not traced
cov_school_typeCovariate not traced
cov_teaching_levelCovariate not traced

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("habibi_2021_pe_tsc")
# Python
pip install irw

import irw
df = irw.fetch("habibi_2021_pe_tsc")

How to cite

@misc{https://doi.org/10.17632/b6zh5mjd5p,
  doi = {10.17632/B6ZH5MJD5P},
  url = {https://data.mendeley.com/datasets/b6zh5mjd5p},
  author = {{Akhmad Habibi}},
  title = {Data on Physical Education Teachers’ Burnout during Pandemic: Structural Equation Modelling },
  publisher = {Mendeley},
  year = {2021},
  copyright = {Creative Commons Attribution 4.0 International}
}

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