10,631 responses from 1,329 respondents to 8 items.
One of 3 tables from the same source; this one is the symptoms table. The others are listed under Related tables.
| Description | 8-item binary (0/1) checklist of symptoms experienced during high air-pollution periods, from a survey of private-sector workers in Ulaanbaatar |
|---|---|
| Reference | Ganbat 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. |
| DOI | 10.1371/journal.pone.0263220 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0263220.s004&type=supplementary |
| Responses | 10,631 |
|---|---|
| Respondents | 1,329 |
| Items | 8 |
| Response categories | 2 |
| Responses per respondent | 7.999 |
| Responses per item | 1328.875 |
| Density | 1.000 |
| Longitudinal | FALSE |
| sample | Workplace |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | mon |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Ulaanbaatar wintertime absenteeism survey: common symptoms related to air pollution (Q1) |
|---|---|
| Mean words per item | 4 |
| Mean characters per item | 26.500 |
| Mean characters per response | 2 |
| Flesch-Kincaid grade level | 4.776 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, ganbat_2022_air_pollution.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.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
id | IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task). | ID |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
What item codes and covariate values stand for is in the source’s codebook, codebook (sheet in S4_File.xlsx).
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("ganbat_2022_pollution_symptoms")
# Python
pip install irw
import irw
df = irw.fetch("ganbat_2022_pollution_symptoms")
@article{Ganbat_2022, title={Integrating quantitative and qualitative approaches to assess wintertime illness-related absenteeism and its direct and indirect costs among the private sector in Ulaanbaatar}, volume={17}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0263220}, DOI={10.1371/journal.pone.0263220}, number={2}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Ganbat, Mandukhai and Erdenebileg, Nasantogtokh and Batbold, Chuluunbileg and Nergui, Saruultuya and Anderson, Ron and Wigfall, Clarence and Amarsanaa, Narantsetseg and Heikens, Alex and Sarantuya, Moiltmaa and Warburton, David}, editor={Jiménez, Natalia}, year={2022}, month=Feb, pages={e0263220} }
| IRW version | v549 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v13.0 |
| Redivis dataset DOI | 10.57761/w8zx-td92 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v39.0 |