andersen_2026_party_masking

12,573 rated or chosen profiles from 1,326 respondents, in up to 5 tasks of 2 profiles varying on 6 attributes.

About this table

DescriptionCandidate choice conjoint fielded on MTurk in the US: respondents chose between two candidates in 5 tasks, with candidate party shown in one between-subject arm and hidden in the other (1,326 respondents). Outcomes: candidate choice, and in the no-party arm's last task a guess of each candidate's party.
ReferenceAndersen, S. C., & Hjortskov, M. (2026). Masking or lexicographic preferences? Using conjoint experiments to distinguish between two fundamental theories of choice. Political Science Research and Methods.
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/NA3YBS

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

Respondents1,326
Rows (one per respondent, task and profile)12,573
Tasks per respondent (most)5
Profiles per task2
Attributes6
Tasks with no profile chosen0

Design

Topiccandidate choice
A profile representsperson
CountryUS
Language shownen
Language of stored levelsen
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceWhich of these two candidates would you choose? If in doubt, please choose the one that you like the most [choice, forced]
rating_guess_democratWhich party do you think the two candidates above are from? If in doubt, please take a guess [rating, 0 (Republican) to 1 (Democrat)]

Columns

attr_ageattr_experienceattr_genderattr_jobattr_partyattr_racechoicecov_party_id7idprofilerating_guess_democrattasktrial_party_info

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("andersen_2026_party_masking", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("andersen_2026_party_masking", source="conj")

How to cite

@misc{https://doi.org/10.7910/dvn/na3ybs,
  doi = {10.7910/DVN/NA3YBS},
  url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/NA3YBS},
  author = {Andersen, Simon Calmar and Larsen, Morten Hjortskov},
  keywords = {Social Sciences, Candidate choice, conjoint, masking, lexicographic preferences, gender, race},
  title = {Replication Data for: Masking or Lexicographic Preferences? Using Conjoint Experiments to Distinguish Between Two Fundamental Theories of Choice},
  publisher = {Harvard Dataverse},
  year = {2026}
}

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