ganbat_2022_pollution_disease_risk

6,645 responses from 1,329 respondents to 5 items.

About this table

Description5-item binary (0/1) checklist of diseases believed to be caused by air pollution, from the same survey of Ulaanbaatar workers
ReferenceGanbat A, Erdenebileg T, Batbold A, et al. (2022) Integrating quantitative and qualitative approaches to assess wintertime illness-related absenteeism and its direct and indirect costs among the private sector in Ulaanbaatar. PLoS ONE.
DOI10.1371/journal.pone.0263220
LicenceCC BY 4.0
Source datahttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0263220.s004&type=supplementary

Size and shape

Responses6,645
Respondents1,329
Items5
Response categories2
Responses per respondent5
Responses per item1,329
Density1
LongitudinalFALSE

Classification

sampleWorkplace
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)mon

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

InstrumentWintertime illness-related absenteeism questionnaire (Ulaanbaatar private-sector employees), Question 2 - diseases experienced during high air pollution periods
Mean words per item1
Mean characters per item2
Mean characters per response2
Flesch-Kincaid grade level-3.400

Columns

iditemresp

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python:

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("ganbat_2022_pollution_disease_risk")
# Python
pip install irw

import irw
df = irw.fetch("ganbat_2022_pollution_disease_risk")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse_4 v7.0
Redivis dataset DOI10.57761/cpj5-vm97
Manifest pin for this IRW versionv7.0
Metadata sourceirw_meta v23.0