34,790 rated or chosen profiles from 2,913 respondents, in up to 6 tasks of 2 profiles varying on 4 attributes.

About this table

Description	Paired conjoint in which 2,913 US partisans (Qualtrics panel) chose between two hypothetical primary candidates of their own party in up to 6 pairs; attributes are the candidate's name (carrying gender and race), chance of winning the general election, ideology rating and age. Outcomes: vote choice (choice) and which candidate best represents the respondent's interests (choice_represent).
Reference	Albert, Z., & Costa, M. (2024). Winning at all costs? How negative partisanship affects voter decision-making. Political Behavior, 47(3), 963-989.
DOI	10.1007/s11109-024-09974-2
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/1TYGV6

Notes

Conjoint table (irw_conjoint_2). choice = which of the two candidates the respondent would vote for, choice_represent = which would best represent the respondent's interests (both paraphrased from the authors' code, forced choice A/B, no opt-out, up to 6 pairs). Attribute text is the piped fragment stored in the export; the ideology set depends on the respondent's party block (trial_primary); independents and attention-check failures have no answers and are omitted. Study 2 (issue-position variant) is not built because only Candidate A's attributes are recorded; N against the article not checked.

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 of the two candidates would you vote for? (paraphrase; authors' 'strategic choice', plot title 'Vote for') [choice, forced]
choice_represent	Which of the two candidates do you think would best represent your interests? (paraphrase; authors' 'sincere choice', plot title 'Thinks represents best') [choice, forced]

Columns

attr_ageattr_ideologyattr_nameattr_winchoicechoice_representcov_attentioncheck2_1cov_attentioncheck2_2cov_attentioncheck2_3cov_attentioncheck2_4cov_attentioncheck2_5cov_edu_codecov_feelingtherm_bidencov_feelingtherm_demscov_feelingtherm_repubscov_feelingtherm_trumpcov_gender_codecov_ideo7cov_partywin_dems_codecov_partywin_repubs_codecov_pid_codecov_pid_ind_codecov_race_codeidprofiletasktrial_primary

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("albert_2024_negative_partisanship", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("albert_2024_negative_partisanship", source="conj")

How to cite

@article{Albert_2024, title={Winning At All Costs? How Negative Partisanship Affects Voter Decision-Making}, volume={47}, ISSN={1573-6687}, url={http://dx.doi.org/10.1007/s11109-024-09974-2}, DOI={10.1007/s11109-024-09974-2}, number={3}, journal={Political Behavior}, publisher={Springer Science and Business Media LLC}, author={Albert, Zachary and Costa, Mia}, year={2024}, month=Nov, pages={963–989} }

Respondents	2,913
Rows (one per respondent, task and profile)	34,790
Tasks per respondent (most)	6
Profiles per task	2
Attributes	4
Tasks with no profile chosen	0

IRW version	v584
Redivis dataset	irw_conjoint_2 v1.4
Redivis dataset DOI	10.57761/hk97-wv30
Manifest pin for this IRW version	v1.4
Metadata source	irw_meta v44.0

albert_2024_negative_partisanship