17,240 rated or chosen profiles from 2,155 respondents, in up to 4 tasks of 2 profiles varying on 6 attributes.
| Description | 2,155 urban Ukrainian adults (Info Sapiens online panel, July 2020) rated 4 pairs of hypothetical local-election candidates described by name (gender and Ukrainian/Russian/neutral-sounding), priority issue, age, family status, children and political experience; yes/no vote intention and yes/no could-win for each candidate. |
|---|---|
| Reference | Erlich, A., & Beauvais, E. (2023). Explaining women's political underrepresentation in democracies with high levels of corruption. Political Science Research and Methods, 11(4), 804-822. |
| DOI | 10.1017/psrm.2022.46 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/P9E3WZ |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 2,155 |
|---|---|
| Rows (one per respondent, task and profile) | 17,240 |
| Tasks per respondent (most) | 4 |
| Profiles per task | 2 |
| Attributes | 6 |
| Topic | candidate choice |
|---|---|
| A profile represents | person |
| Country | UA |
| Language shown | uk, ru |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): age 25 years old never paired with 15 years of political experience (observed in all 8 profile slots; authors' cjoint constraints also list 35 x 15 years, which does occur in the data) |
| 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 | Would you vote for these candidates? (translated) [rating, 0 (No) to 1 (Yes)] |
|---|---|
| rating_win | Do you think these candidates could win local election? (translated) [rating, 0 (No) to 1 (Yes)] |
attr_ageattr_childrenattr_experienceattr_familyattr_issueattr_nameattrpos_ageattrpos_childrenattrpos_experienceattrpos_familyattrpos_issuecov_agecov_gendercov_interview_languagecov_macroregioncov_settlement_typecov_survey_weightidprofileratingrating_wintask
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("erlich_2023_ukraine_candidates", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("erlich_2023_ukraine_candidates", source="conj")
@article{Erlich_2022, title={Explaining women’s political underrepresentation in democracies with high levels of corruption}, volume={11}, ISSN={2049-8489}, url={http://dx.doi.org/10.1017/psrm.2022.46}, DOI={10.1017/psrm.2022.46}, number={4}, journal={Political Science Research and Methods}, publisher={Cambridge University Press (CUP)}, author={Erlich, Aaron and Beauvais, Edana}, year={2022}, month=Oct, pages={804–822} }
| 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 |