kirkland_2018_candidates_yougov

11,432 rated or chosen profiles from 1,200 respondents, in up to 5 tasks of 2 profiles varying on 6 attributes.

One of 2 tables from the same source; this one is the yougov table. The others are listed under Related tables.

About this table

Description1,200 US respondents from a YouGov nationally representative sample (survey weights included) each judged up to 5 hypothetical (mayoral) elections between two candidates described by race, gender, age, political experience, career experience and, in randomly chosen partisan elections only, party; outcomes are a forced choice and a 0-100 competence rating of each candidate.
ReferenceKirkland, P. A., & Coppock, A. (2018). Candidate choice without party labels: New insights from conjoint survey experiments. Political Behavior, 40(3), 571-591.
DOI10.1007/s11109-017-9414-8
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/WSUHI3

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

Respondents1,200
Rows (one per respondent, task and profile)11,432
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
Task orderrecorded in the deposit
Profile positioninferred from row order

Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.

Outcomes:

choiceWhich of these two candidates do you prefer? [choice, forced]
ratingOn a scale from 0 to 100, how competent do you think these candidates would be as mayor? [rating, 0 (unknown) to 100 (unknown)]

Columns

attr_ageattr_genderattr_jobattr_partyattr_politicalattr_racechoicecov_party_idcov_party_id7_codecov_survey_weightidprofileratingtasktrial_election

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("kirkland_2018_candidates_yougov", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("kirkland_2018_candidates_yougov", source="conj")

How to cite

@article{Kirkland_2017, title={Candidate Choice Without Party Labels:: New Insights from Conjoint Survey Experiments}, volume={40}, ISSN={1573-6687}, url={http://dx.doi.org/10.1007/s11109-017-9414-8}, DOI={10.1007/s11109-017-9414-8}, number={3}, journal={Political Behavior}, publisher={Springer Science and Business Media LLC}, author={Kirkland, Patricia A. and Coppock, Alexander}, year={2017}, month=June, pages={571–591} }

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