14,890 rated or chosen profiles from 1,499 respondents, in up to 5 tasks of 2 profiles varying on 5 attributes.
One of 2 tables from the same source; this one is the national table. The others are listed under Related tables.
| Description | Respondents from a US national online sample (1,499) chose between two hypothetical candidates in five pairs varying gender, race, tax position, crime platform (prevention vs tough on crime) and education policy. |
|---|---|
| Reference | Laterzo-Tingley, I., & Christiani, L. (2024). The “tough-on-crime” left: Race, gender, and elections of law-and-order Democrats. Research & Politics, 11(3). |
| DOI | 10.1177/20531680241261765 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/8ENZUC |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 1,499 |
|---|---|
| Rows (one per respondent, task and profile) | 14,890 |
| Tasks per respondent (most) | 5 |
| Profiles per task | 2 |
| Attributes | 5 |
| 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 | unknown |
| Task order | unknown |
| Profile position | unknown |
Outcomes:
| choice | unknown [choice, forced] |
|---|
attr_education_policyattr_genderattr_public_securityattr_raceattr_taxchoicecov_age_groupcov_educationcov_gendercov_ideologycov_party_idcov_party_leancov_party_strong_codecov_racecov_sexualitycov_thermometer_bidencov_thermometer_dem_partycov_thermometer_rep_partycov_thermometer_trumpcov_tough_racistidprofiletask
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("laterzotingley_2024_crime_national", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("laterzotingley_2024_crime_national", source="conj")
@article{Laterzo_Tingley_2024, title={The “tough-on-crime” left: Race, gender, and elections of law-and-order democrats}, volume={11}, ISSN={2053-1680}, url={http://dx.doi.org/10.1177/20531680241261765}, DOI={10.1177/20531680241261765}, number={3}, journal={Research & Politics}, publisher={SAGE Publications}, author={Laterzo-Tingley, Isabel and Christiani, Leah}, year={2024}, 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 |