1,000 rated or chosen profiles from 1,000 respondents, in up to 1 tasks of 1 profile varying on 3 attributes.
| Description | US respondents in the UC Riverside module of the 2020 CCES (n = 1,000) each rated one hypothetical congressional candidate whose PAC-contribution policy (accepts/doesn't), prior elected experience (none/councilmember/mayor) and gender were randomized; candidate party matched the respondent's party. Outcomes: likelihood of voting for, donating to, and trustworthiness of the candidate (7-point). |
|---|---|
| Reference | Landgrave, M., Jenkins, N. R., & Hardesty, A. J. (2025). Do congressional candidates benefit from rejecting PAC contributions? Evidence from a pre-registered candidate evaluation survey experiment. Research & Politics, 12(3). |
| DOI | 10.1177/20531680251383284 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/HEVWIG |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 1,000 |
|---|---|
| Rows (one per respondent, task and profile) | 1,000 |
| Tasks per respondent (most) | 1 |
| Profiles per task | 1 |
| Attributes | 3 |
| Topic | candidate choice |
|---|---|
| A profile represents | person |
| Country | US |
| Language shown | en |
| Language of stored levels | en |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Outcomes:
| rating_donate | Donation likelihood (paraphrase) [rating, 1 (Very Likely) to 7 (Very Unlikely)] |
|---|---|
| rating_trust | Candidate trustworthiness (paraphrase) [rating, 1 (Very Trustworthy) to 7 (Very Untrustworthy)] |
| rating_vote | Vote likelihood (paraphrase) [rating, 1 (Very Likely) to 7 (Very Unlikely)] |
attr_genderattr_pac_contributionsattr_prior_experiencecov_birth_yearcov_educationcov_gendercov_party_idcov_party_id7cov_racecov_statecov_survey_weightidprofilerating_donaterating_trustrating_votetasktrial_candidate_party
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.
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("landgrave_2025_pac_contributions", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("landgrave_2025_pac_contributions", source="conj")
@article{Landgrave_2025, title={Do congressional candidates benefit from rejecting PAC contributions? Evidence from a pre-registered candidate evaluation survey experiment}, volume={12}, ISSN={2053-1680}, url={http://dx.doi.org/10.1177/20531680251383284}, DOI={10.1177/20531680251383284}, number={3}, journal={Research & Politics}, publisher={SAGE Publications}, author={Landgrave, Michelangelo and Jenkins, Nicholas R. and Hardesty, Aubree J.}, year={2025}, month=July }
| 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 |