27,916 rated or chosen profiles from 1,994 respondents, in up to 7 tasks of 2 profiles varying on 5 attributes.

One of 5 tables from the same source; this one is the politicians_korea table. The others are listed under Related tables.

About this table

Description	1994 South Korean online panel respondents each chose between two hypothetical emerging politicians in 7 tasks; profiles are positions on democracy, the economy, welfare, gender roles and ethnic/cultural diversity, shown as statements in Korean.
Reference	Brehm, N., Zhou, A. H., & Denney, S. (2025). From division to democracy: Integrating post-socialist citizens in Germany and South Korea. Communist and Post-Communist Studies.
DOI	10.1525/cpcs.2025.2636997
Licence	CC BY 4.0
Source data	https://doi.org/10.7910/DVN/8GUUP5

Notes

Conjoint table (irw_conjoint). Forced choice between two hypothetical politicians (no opt-out); question wording not deposited. Attribute levels are issue statements in the survey language. Sample as prepared by the authors; one of three samples from the same study, each its own table.

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	1,994
Rows (one per respondent, task and profile)	27,916
Tasks per respondent (most)	7
Profiles per task	2
Attributes	5
Tasks with no profile chosen	0

Design

Topic	candidate choice
A profile represents	person
Country	KR
Language shown	ko
Language of stored levels	ko
Randomization restrictions	unknown
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	Which of the two politicians do you prefer? (paraphrase) [choice, forced]

Columns

attr_democracyattr_diversityattr_economyattr_genderattr_welfarechoicecov_birth_yearcov_educationcov_femalecov_ideologycov_party_votecov_provinceidprofiletask

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("brehm_2025_politicians_korea", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("brehm_2025_politicians_korea", source="conj")

How to cite

@article{Brehm_2025, title={From Division to Democracy}, ISSN={1873-6920}, url={http://dx.doi.org/10.1525/cpcs.2025.2636997}, DOI={10.1525/cpcs.2025.2636997}, journal={Communist and Post-Communist Studies}, publisher={University of California Press}, author={Brehm, Robin and Zhou, Tianzi and Denney, Steven}, year={2025}, pages={1–24} }

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