44,180 rated or chosen profiles from 2,209 respondents, in up to 10 tasks of 2 profiles varying on 8 attributes.
One of 2 tables from the same source; this one is the candidates table. The others are listed under Related tables.
| Description | 2,209 US Prolific respondents (December 2020) each rated 10 pairs of hypothetical congressional candidates from their own party on a 7-point vote-likelihood scale; 8 attributes including a sexual-harassment scandal, under fully or partially randomized designs. |
|---|---|
| Reference | Horiuchi, Y., Markovich, Z., & Yamamoto, T. (2022). Does conjoint analysis mitigate social desirability bias? Political Analysis, 30(4), 535-549. |
| DOI | 10.1017/pan.2021.30 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/4WDVDB |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 2,209 |
|---|---|
| Rows (one per respondent, task and profile) | 44,180 |
| Tasks per respondent (most) | 10 |
| Profiles per task | 2 |
| Attributes | 8 |
| Topic | candidate choice |
|---|---|
| A profile represents | person |
| Country | US |
| Language shown | en |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): unchanged |
| 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 | How likely or unlikely do you think you would be to vote for each candidate from the [Democratic/Republican] Party instead of the candidate from the [Republican/Democratic] Party in each of the two elections? [rating, 1 (Very Unlikely) to 7 (Very Likely)] |
|---|
attr_ageattr_genderattr_past_political_experienceattr_previous_professionattr_raceattr_residencyattr_scandalattr_undergraduate_degreeattrpos_ageattrpos_genderattrpos_past_political_experienceattrpos_previous_professionattrpos_raceattrpos_residencyattrpos_scandalattrpos_undergraduate_degreecov_age_groupcov_attention_pass_1cov_attention_pass_2cov_dem_strengthcov_educationcov_gendercov_ideologycov_incomecov_party_idcov_party_leancov_racecov_rep_strengthidprofileratingtasktrial_candidate_partytrial_designtrial_prime
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("horiuchi_2022_sdb_candidates", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("horiuchi_2022_sdb_candidates", source="conj")
@article{Horiuchi_2021, title={Does Conjoint Analysis Mitigate Social Desirability Bias?}, volume={30}, ISSN={1476-4989}, url={http://dx.doi.org/10.1017/pan.2021.30}, DOI={10.1017/pan.2021.30}, number={4}, journal={Political Analysis}, publisher={Cambridge University Press (CUP)}, author={Horiuchi, Yusaku and Markovich, Zachary and Yamamoto, Teppei}, year={2021}, month=Sept, pages={535–549} }
| 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 |