cao_2024_mbi

7,172 responses from 326 respondents to 22 items.

One of 3 tables from the same source; this one measures Burnout (MBI-ES). The others are listed under Related tables.

About this table

DescriptionMaslach Burnout Inventory, Educators Survey (22 items: emotional exhaustion, depersonalization, personal accomplishment; stored 1-5) for 326 Chinese university lecturers.
ReferenceCao, B. (2024). THE EFFECT OF SOCIAL SUPPORT ON BURNOUT AMONG CHINESE UNIVERSITY LECTURERS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.12788010
LicenceCC BY 4.0
Source datahttps://zenodo.org/records/12788010

Size and shape

Responses7,172
Respondents326
Items22
Response categories5
Responses per respondent22
Responses per item326
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleGeneral/non-specific, Workplace
construct typeAffective/mental health
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)chi
construct nameBurnout (MBI-ES)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, cao_2024_burnout.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_age_bandCovariate Q2
cov_educationCovariate Q3
cov_genderCovariate Q1
cov_income_bandCovariate Q6
cov_maritalCovariate Q4
cov_teaching_years_bandCovariate Q7
cov_university_typeCovariate Q5

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("cao_2024_mbi")
# Python
pip install irw

import irw
df = irw.fetch("cao_2024_mbi")

How to cite

@misc{https://doi.org/10.5281/zenodo.12788010,
  doi = {10.5281/ZENODO.12788010},
  url = {https://zenodo.org/doi/10.5281/zenodo.12788010},
  author = {Cao, Beibei},
  title = {THE EFFECT OF SOCIAL SUPPORT ON BURNOUT AMONG CHINESE UNIVERSITY LECTURERS},
  publisher = {Zenodo},
  year = {2024},
  copyright = {Creative Commons Attribution 4.0 International}
}

Version and provenance

IRW versionv584
Redivis datasetitem_response_warehouse_6 v5.11
Redivis dataset DOI10.57761/qahq-j855
Manifest pin for this IRW versionv5.11
Metadata sourceirw_meta v44.0