7,560 rated or chosen profiles from 756 respondents, in up to 5 tasks of 2 profiles varying on 4 attributes.

About this table

Description	756 respondents in Myanmar (June 2022) each chose between 5 pairs of hypothetical 2023 scenarios varying the COVID-19 situation, vaccine availability, access to medical services, and whether elections are restored.
Reference	Mon, S. O., & Yamada, K. (2024). Do pandemics reduce support for democracy? A survey experiment in Myanmar. Japanese Journal of Political Science, 25(3), 140-161.
DOI	10.1017/S1468109924000069
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/YUZSDO

Notes

Conjoint table (irw_conjoint). choice = which scenario was selected as preferred (wording not deposited), forced choice, no opt-out. Attribute levels are associated and unequal in the data (pre-generated choice design), so estimate with all attributes in the model. Level text rebuilt from dummies with codebook wording (English; display language undocumented); the codebook's attribute names for A3/A4 are swapped relative to the Stata labels, which were followed.

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Respondents	756
Rows (one per respondent, task and profile)	7,560
Tasks per respondent (most)	5
Profiles per task	2
Attributes	4
Tasks with no profile chosen	0

Design

Topic	democracy institutions, health covid
A profile represents	scenario
Country	MM
Language shown	unknown
Language of stored levels	en
Randomization restrictions	unknown
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	1 if the profile is selected as the preferred alternative (paraphrase) [choice, forced]

Columns

attr_covid_situationattr_governmentattr_medical_servicesattr_vaccinechoiceidprofiletask

The layout (one row per respondent, task and profile; choice/rating outcomes; attr_ columns holding each attribute level as displayed) is defined in the IRW conjoint standard. What the outcomes asked is under Design above.

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant 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("mon_2024_covid_democracy", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("mon_2024_covid_democracy", source="conj")

How to cite

@article{Mon_2024, title={Do pandemics reduce support for democracy? A survey experiment in Myanmar}, volume={25}, ISSN={1474-0060}, url={http://dx.doi.org/10.1017/S1468109924000069}, DOI={10.1017/s1468109924000069}, number={3}, journal={Japanese Journal of Political Science}, publisher={Cambridge University Press (CUP)}, author={Mon, Swe Oo and Yamada, Kyohei}, year={2024}, month=Aug, pages={140–161} }

Version and provenance

IRW version	v584
Redivis dataset	irw_conjoint v4.2
Redivis dataset DOI	10.57761/qkye-q530
Manifest pin for this IRW version	v4.2
Metadata source	irw_meta v44.0