9,370 rated or chosen profiles from 1,567 respondents, in up to 6 tasks of 1 profile varying on 8 attributes.
One of 2 tables from the same source; this one is the scotus_nominees table. The others are listed under Related tables.
| Description | US adults (SSI online sample, December 2013; 1,567 respondents) each rated six single hypothetical US Supreme Court nominee profiles varying work experience, gender, age, race/ethnicity, clerkship, law school rank and religion, plus party leaning in a randomly assigned partisan arm; outcomes are 5-point support, qualification and trust ratings (Sen 2017 experiment). |
|---|---|
| Reference | Acharya, A., Blackwell, M., & Sen, M. (2018). Analyzing causal mechanisms in survey experiments. Political Analysis, 26(4), 357-378. |
| DOI | 10.1017/pan.2018.19 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/KHE44F |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 1,567 |
|---|---|
| Rows (one per respondent, task and profile) | 9,370 |
| Tasks per respondent (most) | 6 |
| Profiles per task | 1 |
| Attributes | 8 |
| 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 | 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 | On a scale from strongly oppose to strongly support, where would you place your level of support for... (truncated in source) [rating, 1 (Strongly oppose) to 5 (Strongly support)] |
|---|---|
| rating_qualified | On a scale from highly unqualified to highly qualified, where would you place your assessment of thi... (truncated in source) [rating, 1 (Highly unqualified) to 5 (Highly qualified)] |
| rating_trust | On a scale from strongly mistrust to strongly trust, how much would you trust that this potential ca... (truncated in source) [rating, 1 (Strongly mistrust) to 5 (Strongly trust)] |
attr_ageattr_clerkshipattr_genderattr_legal_educationattr_party_leaningattr_raceattr_religionattr_work_experienceattrpos_ageattrpos_clerkshipattrpos_genderattrpos_legal_educationattrpos_party_leaningattrpos_raceattrpos_religionattrpos_work_experiencecov_agecov_court_awarenesscov_educationcov_gendercov_incomecov_party_id7cov_racecov_religioncov_stateidprofileratingrating_qualifiedrating_trusttasktrial_arm
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("acharya_2018_scotus_nominees", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("acharya_2018_scotus_nominees", source="conj")
@article{Acharya_2018, title={Analyzing Causal Mechanisms in Survey Experiments}, volume={26}, ISSN={1476-4989}, url={http://dx.doi.org/10.1017/pan.2018.19}, DOI={10.1017/pan.2018.19}, number={4}, journal={Political Analysis}, publisher={Cambridge University Press (CUP)}, author={Acharya, Avidit and Blackwell, Matthew and Sen, Maya}, year={2018}, month=Aug, pages={357–378} }
| 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 |