19,940 rated or chosen profiles from 997 respondents, in up to 10 tasks of 2 profiles varying on 5 attributes.

About this table

Description	US adults (Prolific, June 2022; n=997) compared 10 pairs of hypothetical state supreme court candidates described by prior political experience, law school, gender, occupation and newspaper endorsement; outcomes are a forced choice of preferred candidate and a 4-point rating of each candidate's qualification.
Reference	Kennedy, M., Nelson, M. J., & Heidt-Forsythe, E. (2025). Gender stereotypes and candidate qualifications in judicial elections. Political Research Quarterly, 78(1).
DOI	10.1177/10659129241275458
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/JVIARV

Notes

choice = "Which candidate do you prefer?" (forced, no opt-out; tasks 4 and 7 carry a leftover "Which job offer do you prefer?" export label); rating = "How qualified is Candidate 1/2 to be a state high court justice?" 4-point, reversed from source so 4 = most qualified (as the authors did; anchor wording not in deposit). 15 tasks from 2 respondents lack a choice; attribute order randomized per respondent, no documented restrictions. IP, location, Prolific/Qualtrics IDs, free text and timers dropped; article N not checked. (irw#2887)

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

Design

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

Outcomes:

choice	Which candidate do you prefer? [choice, forced]
rating	How qualified is Candidate 1 to be a state high court justice? [rating, 1 (unknown) to 4 (unknown)]

Columns

attr_educationattr_endorsementattr_genderattr_occupationattr_political_experienceattrpos_educationattrpos_endorsementattrpos_genderattrpos_occupationattrpos_political_experiencechoicecov_attention1cov_birth_yearcov_child18cov_education_codecov_gender_codecov_hispaniccov_ideologycov_knowctcov_knowgender_1cov_knowgender_2cov_knowgovcov_knowlastsaycov_knowlegcov_maritalcov_mobilizationcov_party_id_codecov_party_leancov_party_strength_demcov_party_strength_repcov_polinterestcov_racecov_sexism_1cov_sexism_2cov_sexism_3cov_sexism_4cov_sexism_5cov_statecov_votefreqidprofileratingtask

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("kennedy_2025_judicial_candidates", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("kennedy_2025_judicial_candidates", source="conj")

How to cite

@article{Kennedy_2024, title={Gender Stereotypes and Candidate Qualifications in Judicial Elections}, volume={78}, ISSN={1938-274X}, url={http://dx.doi.org/10.1177/10659129241275458}, DOI={10.1177/10659129241275458}, number={1}, journal={Political Research Quarterly}, publisher={SAGE Publications}, author={Kennedy, Megan and Nelson, Michael J. and Heidt-Forsythe, Erin}, year={2024}, month=Oct, pages={182–197} }

Respondents	997
Rows (one per respondent, task and profile)	19,940
Tasks per respondent (most)	10
Profiles per task	2
Attributes	5
Tasks with no profile chosen	0

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

kennedy_2025_judicial_candidates