28,850 rated or chosen profiles from 962 respondents, in up to 15 tasks of 2 profiles varying on 6 attributes.
| Description | 962 US MTurk respondents (Nov 2019) each compared 15 pairs of hypothetical congressional candidates differing in party, four issue positions and support for one of five court-curbing proposals (10 general-election, 5 primary pairs); outcomes are the preferred candidate and two 5-point ratings (candidate rating, mobilization). |
|---|---|
| Reference | Driscoll, A., & Nelson, M. J. (2023). The costs of court curbing: Evidence from the United States. The Journal of Politics, 85(2), 609-624. |
| DOI | 10.1086/723021 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/JGST70 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 962 |
|---|---|
| Rows (one per respondent, task and profile) | 28,850 |
| Tasks per respondent (most) | 15 |
| Profiles per task | 2 |
| Attributes | 6 |
| Tasks with no profile chosen | 0 |
| Topic | candidate choice |
|---|---|
| A profile represents | person |
| Country | US |
| Language shown | en |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): the two candidates in a pair always take opposite positions on every issue and on court curbing; opposite parties in trials 1-10, same party (the respondent's) in trials 11-15; the court-curbing proposal is set by trial number |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Because the attribute levels were not randomized independently, an estimator has to account for the restriction; a plain difference in means across levels can mislead.
Outcomes:
| choice | Which of the two candidates would you be more likely to support? (paraphrase) [choice, forced] |
|---|---|
| rating | Candidate Rating Outcome (paraphrase) [rating, 1 (unknown) to 5 (unknown)] |
| rating_mobilize | Mobilization Outcome (paraphrase) [rating, 1 (unknown) to 5 (unknown)] |
attr_chinaattr_courtattr_educationattr_gunsattr_lgbtattr_partychoicecov_ideologycov_party_id7_codeidprofileratingrating_mobilizetasktrial_court_proposaltrial_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.
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("driscoll_2023_court_curbing", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("driscoll_2023_court_curbing", source="conj")
@article{Driscoll_2023, title={The Costs of Court Curbing: Evidence from the United States}, volume={85}, ISSN={1468-2508}, url={http://dx.doi.org/10.1086/723021}, DOI={10.1086/723021}, number={2}, journal={The Journal of Politics}, publisher={University of Chicago Press}, author={Driscoll, Amanda and Nelson, Michael J.}, year={2023}, month=Apr, pages={609–624} }
| 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 |