78,000 rated or chosen profiles from 7,800 respondents, in up to 5 tasks of 2 profiles varying on 6 attributes.
| Description | YouGov respondents in eight US metro areas (Jan 2018, n = 7,800) chose between 5 pairs of hypothetical local development plans varying in education, higher-education, investment/tax, governance, worker/entrepreneur and local-service policies; forced choice. |
|---|---|
| Reference | Jensen, A., Marble, W., Scheve, K., & Slaughter, M. J. (2021). City limits to partisan polarization in the American public. Political Science Research and Methods, 9(2), 223-241. |
| DOI | 10.1017/psrm.2020.56 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/JYKRGY |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 7,800 |
|---|---|
| Rows (one per respondent, task and profile) | 78,000 |
| Tasks per respondent (most) | 5 |
| Profiles per task | 2 |
| Attributes | 6 |
| Tasks with no profile chosen | 0 |
| Topic | housing neighbourhoods |
|---|---|
| A profile represents | policy |
| Country | US |
| Language shown | en |
| Language of stored levels | en |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Outcomes:
| choice | For each comparison, please indicate which of the two plans you prefer. Please just tell us which one you like best. You may like both or not like either one. In any case, choose the one you prefer the most. [choice, forced] |
|---|
attr_educationattr_governanceattr_higher_educationattr_investment_taxesattr_local_servicesattr_workers_entrepreneurschoicecov_birth_yearcov_citycov_educationcov_gendercov_ideologycov_party_idcov_party_leancov_party_strengthcov_racecov_survey_weightcov_us_borncov_vote_2016idprofiletask
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("jensen_2021_city_development_plans", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("jensen_2021_city_development_plans", source="conj")
@article{Jensen_2021, title={City limits to partisan polarization in the American public}, volume={9}, ISSN={2049-8489}, url={http://dx.doi.org/10.1017/psrm.2020.56}, DOI={10.1017/psrm.2020.56}, number={2}, journal={Political Science Research and Methods}, publisher={Cambridge University Press (CUP)}, author={Jensen, Amalie and Marble, William and Scheve, Kenneth and Slaughter, Matthew J.}, year={2021}, month=Feb, pages={223–241} }
| 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 |