brehm_2025_politicians_germany

33,136 rated or chosen profiles from 2,071 respondents, in up to 8 tasks of 2 profiles varying on 5 attributes.

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

About this table

Description2071 German respondents (quota sample plus an Eastern German oversample) each chose between two hypothetical emerging politicians in 8 tasks; profiles are positions on democracy, the economy, welfare, gender roles and ethnic/cultural diversity, shown as statements in German.
ReferenceBrehm, 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.
DOI10.1525/cpcs.2025.2636997
LicenceCC BY 4.0
Source datahttps://doi.org/10.7910/DVN/8GUUP5

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

Respondents2,071
Rows (one per respondent, task and profile)33,136
Tasks per respondent (most)8
Profiles per task2
Attributes5
Tasks with no profile chosen0

Design

Topiccandidate choice
A profile representsperson
CountryDE
Language shownde
Language of stored levelsde
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

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

Columns

attr_democracyattr_diversityattr_economyattr_genderattr_welfarechoicecov_birth_countrycov_birth_yearcov_current_statecov_educationcov_femalecov_party_votecov_region_groupcov_state_at_18idprofiletask

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("brehm_2025_politicians_germany", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("brehm_2025_politicians_germany", 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 versionv584
Redivis datasetirw_conjoint v4.2
Redivis dataset DOI10.57761/qkye-q530
Manifest pin for this IRW versionv4.2
Metadata sourceirw_meta v44.0