21,218 rated or chosen profiles from 1,784 respondents, in up to 6 tasks of 2 profiles varying on 9 attributes.
One of 2 tables from the same source; this one is the jan22 table. The others are listed under Related tables.
| Description | Paired conjoint in which 1,784 Japanese adults (Rakuten Insight, January 2022) compared hypothetical House of Representatives candidates varying in gender, party, age, experience, education, marital status, children and residence; three tasks asked which candidate is more desirable and three which is more likely to win. Outcomes: choice (preference), choice_expect (expectation). |
|---|---|
| Reference | Kato, G., Lu, F., & Endo, M. (2025). The preference-expectation gap in support for female candidates: Evidence from Japan. Public Opinion Quarterly, 89(1), 217-228. |
| DOI | 10.1093/poq/nfaf002 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/GJWDVM |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 1,784 |
|---|---|
| Rows (one per respondent, task and profile) | 21,218 |
| Tasks per respondent (most) | 6 |
| Profiles per task | 2 |
| Attributes | 9 |
| Tasks with no profile chosen | 0 |
| Topic | candidate choice |
|---|---|
| A profile represents | person |
| Country | JP |
| Language shown | ja |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): attr_party_type is determined by attr_party (LDP, Komeito = Ruling; Independent, CDP, DPP, Ishin, JCP = Oppostion), so Ruling appears on 2/7 of profiles; other attributes show no empty combinations. |
| Task order | inferred from row order |
| 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. Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.
Outcomes:
| choice | Which of the following two persons do you think is more desirable as a Single Member District member of the House of Representatives? Even if you are not entirely sure, please indicate which of the two you would think is more desirable. (translated) [choice, forced] |
|---|---|
| choice_expect | Which of the following two persons do you think is more likely to win in a Single Member District election of the House of Representatives? Even if you are not entirely sure, please indicate which of the two you would think is more likely to win. (translated) [choice, forced] |
attr_ageattr_educationattr_experienceattr_genderattr_marriageattr_partyattr_party_typeattr_residenceattr_youngest_childchoicechoice_expectcov_gendercov_genderval_1cov_genderval_2cov_genderval_3cov_genderval_4cov_genderval_5cov_genderval_6cov_genderval_7cov_genderval_8cov_genderval_9idprofiletasktrial_block_order
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("kato_2025_candidates_jan22", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("kato_2025_candidates_jan22", source="conj")
@article{Kato_2025, title={The Preference-Expectation Gap in Support for Female Candidates: Evidence from Japan}, volume={89}, ISSN={1537-5331}, url={http://dx.doi.org/10.1093/poq/nfaf002}, DOI={10.1093/poq/nfaf002}, number={1}, journal={Public Opinion Quarterly}, publisher={Oxford University Press (OUP)}, author={Kato, Gento and Lu, Fan and Endo, Masahisa}, year={2025}, pages={217–228} }
| 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 |