littmann_2026_terror_women_candidates

16,544 rated or chosen profiles from 1,034 respondents, in up to 8 tasks of 2 profiles varying on 5 attributes.

About this table

DescriptionDanish adults (Epinion omnibus, n = 1,034, Oct-Nov 2024) chose between two fictitious candidates for their own party in 8 tasks, varying gender, age, country of origin, education and political experience; half first read the intelligence service's assessment that the terrorist threat is significant (trial_terror_threat).
ReferenceLittmann, B., Nielsen, N. N. J., & Laustsen, L. (2026). Terrorist threat decreases citizens' relative preferences for women candidates: Insights from a Danish conjoint experiment. European Journal of Political Research.
DOI10.1017/S1475676526101649
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/F7WN3A

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

Respondents1,034
Rows (one per respondent, task and profile)16,544
Tasks per respondent (most)8
Profiles per task2
Attributes5
Tasks with no profile chosen0

Design

Topiccandidate choice
A profile representsperson
CountryDK
Language shownda
Language of stored levelsda
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceWhich of the two candidates would you prefer as the leading candidate for your party in the upcoming parliamentary election? (translated) [choice, forced]

Columns

attr_ageattr_educationattr_genderattr_originattr_political_experiencechoicecov_agecov_age_groupcov_educationcov_gendercov_regioncov_vote_intentioncov_vote_lastidprofiletasktrial_terror_threat

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("littmann_2026_terror_women_candidates", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("littmann_2026_terror_women_candidates", source="conj")

How to cite

@article{Littmann_2026, title={Terrorist threat decreases citizens’ relative preferences for women candidates: Insights from a Danish conjoint experiment}, ISSN={1475-6765}, url={http://dx.doi.org/10.1017/S1475676526101649}, DOI={10.1017/s1475676526101649}, journal={European Journal of Political Research}, publisher={Cambridge University Press (CUP)}, author={Littmann, Brian and Nielsen, Nadia Natasha James and Laustsen, Lasse}, year={2026}, month=Sept, pages={1–14} }

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