qi_2025_health_redistribution

29,484 rated or chosen profiles from 1,638 respondents, in up to 9 tasks of 2 profiles varying on 4 attributes.

About this table

DescriptionUS online respondents (August 2023) chose between two hypothetical Medicare-based health-insurance programs in 9 pairs, varying the transition from private insurance, who benefits most, who bears the tax burden, and total cost. 1,638 respondents; forced binary choice.
ReferenceQi, H. (2025). Understanding public attitudes toward redistribution: Analysis by ideological subgroups. Public Opinion Quarterly.
DOI10.1093/poq/nfaf046
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/ABP4XA

Notes

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

Size and shape

Respondents1,638
Rows (one per respondent, task and profile)29,484
Tasks per respondent (most)9
Profiles per task2
Attributes4
Tasks with no profile chosen0

Design

Topichealth covid, tax welfare economy
A profile representspolicy
CountryUS
Language shownen
Language of stored levelsen
Randomization restrictionsunknown
Task orderinferred from row order
Profile positioninferred from row order

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

Outcomes:

choiceWhich of the two programs do you prefer? (paraphrase) [choice, forced]

Columns

attr_costattr_distributionattr_tax_burdenattr_transitionchoicecov_agecov_age_groupcov_attention_pass_1cov_attention_pass_2cov_attention_pass_3cov_attention_pass_4cov_duration_seccov_educationcov_employmentcov_gendercov_ideologycov_incomecov_party_idcov_party_id7cov_political_attentioncov_racecov_screener_countidprofiletask

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 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("qi_2025_health_redistribution", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("qi_2025_health_redistribution", source="conj")

How to cite

@article{Qi_2025, title={Understanding Public Attitudes Toward Redistribution: Analysis by Ideological Subgroups}, volume={89}, ISSN={1537-5331}, url={http://dx.doi.org/10.1093/poq/nfaf046}, DOI={10.1093/poq/nfaf046}, number={3}, journal={Public Opinion Quarterly}, publisher={Oxford University Press (OUP)}, author={Qi, Hang}, year={2025}, pages={902–914} }

Version and provenance

IRW versionv607
Redivis datasetirw_conjoint v4.2
Redivis dataset DOI10.57761/qkye-q530
Manifest pin for this IRW versionv4.2
Metadata sourceirw_meta v47.0