5,703 rated or chosen profiles from 5,703 respondents, in up to 1 tasks of 1 profile varying on 2 attributes.
| Description | 5,703 US partisans (Qualtrics panel) each read one vignette of a fictitious candidate, William Richardson, who was from the respondent's party or the other party, with his issue positions (moderate or extreme) and/or his party shown or not, and rated him on a 0-100 feeling thermometer. |
|---|---|
| Reference | Lelkes, Y. (2021). Policy over party: Comparing the effects of candidate ideology and party on affective polarization. Political Science Research and Methods, 9(1). |
| DOI | 10.1017/psrm.2019.18 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/V7HWP4 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 5,703 |
|---|---|
| Rows (one per respondent, task and profile) | 5,703 |
| 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 | yes (stated by the source): The issue positions shown follow the candidate's party side (Republican candidate: conservative text; Democrat: liberal text), and the candidate's party is the respondent's party (in-party experiment) or the other party (out-party experiment); the party attribute is therefore determined by the respondent's party and the experiment, and level text is conditional. The control cell (neither shown) is about twice as large as other cells in the out-party experiment. |
| 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:
| rating_thermometer | On a scale ranging from 0 to 100, where a score of 0 means you feel "cold" towards this candidate, a score of 50 means you do not feel "particularly warm or cold" towards this candidate, and a score of 100 means you have "warm" feelings towards this candidate, how would score your feelings towards this candidate? [rating, 0 (cold) to 100 (warm)] |
|---|
attr_ideologyattr_partycov_age_groupcov_gendercov_incomecov_party_idcov_raceidprofilerating_thermometertasktrial_candidate_relation
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("lelkes_2021_candidate_ideology_party", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("lelkes_2021_candidate_ideology_party", source="conj")
@article{Lelkes_2019, title={Policy over party: comparing the effects of candidate ideology and party on affective polarization}, volume={9}, ISSN={2049-8489}, url={http://dx.doi.org/10.1017/psrm.2019.18}, DOI={10.1017/psrm.2019.18}, number={1}, journal={Political Science Research and Methods}, publisher={Cambridge University Press (CUP)}, author={Lelkes, Yphtach}, year={2019}, month=May, pages={189–196} }
| 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 |