60,792 rated or chosen profiles from 7,599 respondents, in up to 4 tasks of 2 profiles varying on 5 attributes.

About this table

Description	Online panel respondents in Australia, Belgium, Canada, France, Switzerland and the USA (Qualtrics, winter 2020/21, n = 7,599, minority language groups oversampled) chose between 4 pairs of COVID-19 decision-making scenarios varying the responsible level of government, the influence of scientists, citizens and stakeholders, and transparency; outcome is the preferred scenario.
Reference	Mueller, S., & Bundi, P. (2024). Multilingual federalism in times of crisis. Regional & Federal Studies, 35(4), 631-665.
DOI	10.1080/13597566.2024.2314081
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/2QS1TA

Notes

Conjoint table (irw_conjoint). choice = which of two scenarios for taking COVID-19 response decisions the respondent preferred (paraphrase; forced choice, no opt-out), 4 tasks. Task/profile inferred from row order; attribute order fixed; level text = article Table 1 English with country-specific government terms. Six countries pooled (cov_country); N = 7,599 as in the article; cov_attention_pass from the authors' Covid knowledge check.

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	7,599
Rows (one per respondent, task and profile)	60,792
Tasks per respondent (most)	4
Profiles per task	2
Attributes	5
Tasks with no profile chosen	0

Design

Topic	health covid, democracy institutions
A profile represents	policy
Country	AU, BE, CA, FR, CH, US
Language shown	nl, en, fr, de
Language of stored levels	en
Randomization restrictions	unknown
Task order	inferred from row order
Profile position	inferred from row order

Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.

Outcomes:

choice	Which of the two scenarios of how COVID-19 responses should be taken do you prefer? (paraphrase) [choice, forced]

Columns

attr_citizensattr_levelattr_scientistsattr_stakeholdersattr_transparencychoicecov_age_groupcov_attention_passcov_countrycov_education_levelcov_gendercov_language_groupcov_leftright_bandcov_lifecov_mother_tonguecov_placecov_regioncov_religionidprofiletask

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("mueller_2024_covid_governance", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("mueller_2024_covid_governance", source="conj")

How to cite

@article{Mueller_2024, title={Multilingual federalism in times of crisis}, volume={35}, ISSN={1743-9434}, url={http://dx.doi.org/10.1080/13597566.2024.2314081}, DOI={10.1080/13597566.2024.2314081}, number={4}, journal={Regional &amp; Federal Studies}, publisher={Informa UK Limited}, author={Mueller, Sean and Bundi, Pirmin}, year={2024}, month=Feb, pages={631–665} }

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