540 rated or chosen profiles from 540 respondents, in up to 1 tasks of 1 profile varying on 2 attributes.
| Description | 540 US Lucid respondents each read one vignette about a county judge candidate (Mary or Michael Bailey) running on a criminal-justice reform or a tough-on-crime platform. Eleven ratings: vote, chance of winning, impartiality, four task-performance items (1-7) and four crime/incarceration items. |
|---|---|
| Reference | Gunderson, A., Kim, J., Lane, E., Bauer, N., Davis, B., & Searles, K. (2025). Candidate gender, campaign appeals, and voter support in judicial elections. Journal of Politics, 87(1), 172-189. |
| DOI | 10.1086/730721 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/dvn/syfwxb |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 540 |
|---|---|
| Rows (one per respondent, task and profile) | 540 |
| Tasks per respondent (most) | 1 |
| Profiles per task | 1 |
| Attributes | 2 |
| Topic | candidate choice |
|---|---|
| A profile represents | person |
| Country | US |
| Language shown | en |
| Language of stored levels | en |
| Randomization restrictions | unknown |
| Task order | inferred from row order |
| Profile position | inferred from row order |
Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.
Outcomes:
| rating_crime_property | On a scale from very well to not well at all, please rate how you think the candidate will address the following issues. - Property crime (such as vandalism) [rating, 1 (most positive option, e.g. very likely / very well (inferred)) to 4 (least positive option (inferred))] |
|---|---|
| rating_crime_recidivism | On a scale from very well to not well at all, please rate how you think the candidate will address the following issues. - Recidivism (i.e. the likelihood of offenders reoffending) [rating, 1 (most positive option, e.g. very likely / very well (inferred)) to 4 (least positive option (inferred))] |
| rating_crime_violent | On a scale from very well to not well at all, please rate how you think the candidate will address the following issues. - Violent crime [rating, 1 (most positive option, e.g. very likely / very well (inferred)) to 4 (least positive option (inferred))] |
| rating_impartial | How much would you trust the candidate to reach impartial decisions regardless of the parties or issues involved in the cases? [rating, 1 (most positive option, e.g. very likely / very well (inferred)) to 4 (least positive option (inferred))] |
| rating_perf_fair_trial | On a scale from 1 to 7 (very ineffective to very effective), please rate how you think the candidate will perform at the following tasks. - Ensure that all citizens before the candidate receive a fair trial [rating, 1 (very ineffective) to 7 (very effective)] |
| rating_perf_people_like_candidate | On a scale from 1 to 7 (very ineffective to very effective), please rate how you think the candidate will perform at the following tasks. - Represent the interests of people like the candidate [rating, 1 (very ineffective) to 7 (very effective)] |
| rating_perf_safety | On a scale from 1 to 7 (very ineffective to very effective), please rate how you think the candidate will perform at the following tasks. - Ensure the safety of Green County citizens [rating, 1 (very ineffective) to 7 (very effective)] |
| rating_perf_your_interests | On a scale from 1 to 7 (very ineffective to very effective), please rate how you think the candidate will perform at the following tasks. - Represent your interests [rating, 1 (very ineffective) to 7 (very effective)] |
| rating_reduce_incarceration | How likely do you think it is that the candidate will reduce incarceration? [rating, 1 (most positive option, e.g. very likely / very well (inferred)) to 4 (least positive option (inferred))] |
| rating_vote | How likely are you to vote for the candidate? [rating, 1 (most positive option, e.g. very likely / very well (inferred)) to 4 (least positive option (inferred))] |
| rating_win_chance | What do you think the candidate's chance of winning is? [rating, 1 (most positive option, e.g. very likely / very well (inferred)) to 4 (least positive option (inferred))] |
attr_candidateattr_platformcov_agecov_genderidprofilerating_crime_propertyrating_crime_recidivismrating_crime_violentrating_impartialrating_perf_fair_trialrating_perf_people_like_candidaterating_perf_safetyrating_perf_your_interestsrating_reduce_incarcerationrating_voterating_win_chancetask
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("gunderson_2025_judicial_appeals", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("gunderson_2025_judicial_appeals", source="conj")
@article{Gunderson_2025, title={Candidate Gender, Campaign Appeals, and Voter Support in Judicial Elections}, volume={87}, ISSN={1468-2508}, url={http://dx.doi.org/10.1086/730721}, DOI={10.1086/730721}, number={1}, journal={The Journal of Politics}, publisher={University of Chicago Press}, author={Gunderson, Anna and Kim, Jeong Hyun and Lane, Elizabeth and Bauer, Nichole and Davis, Belinda and Searles, Kathleen}, year={2025}, month=Jan, pages={172–189} }
| 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 |