44,000 rated or chosen profiles from 2,200 respondents, in up to 10 tasks of 2 profiles varying on 9 attributes.
| Description | Japanese adults (online panel, November 2015; n=2,200) chose between 10 pairs of hypothetical national legislators described by party, age, gender, terms, experience, hometown, education, prior occupation and parental political background, for one house (lower or upper) and both electoral tiers (district and PR, 5 tasks each); attribute text is the Japanese shown. |
|---|---|
| Reference | Horiuchi, Y., Smith, D. M., & Yamamoto, T. (2020). Identifying voter preferences for politicians' personal attributes: A conjoint experiment in Japan. Political Science Research and Methods, 8(1), 75-91. |
| DOI | 10.1017/psrm.2018.26 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/KCIADO |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 2,200 |
|---|---|
| Rows (one per respondent, task and profile) | 44,000 |
| Tasks per respondent (most) | 10 |
| 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 | ja |
| Randomization restrictions | observed in the data (not documented by the source): experience '在職経験なし' (never in office) occurs if and only if terms = 'なし' (none); hometown/occupation/parent level text is tailored to the respondent's prefecture; level weights: terms 'なし' 14.5% vs about 28% for each other level, experience '在職経験なし' 14.5% vs about 43% |
| 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:
| choice | 次の２人の人物のうち、どちらがより小選挙区選出の衆議院議員として望ましいと思いますか。もし、どちらが望ましいかはっきりとは言えない場合でも、どちらか一方、あえていえばより望ましいと思われる方を選んでください。 (lower house, district-tier wording; house and tier words vary by block) [choice, forced] |
|---|
attr_ageattr_educationattr_experienceattr_genderattr_hometownattr_occupationattr_parentattr_partyattr_termsattrpos_ageattrpos_educationattrpos_experienceattrpos_genderattrpos_hometownattrpos_occupationattrpos_parentattrpos_partyattrpos_termschoicecov_agecov_educationcov_gendercov_incomecov_prefectureidprofiletasktrial_housetrial_tier
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_2020_japan_politicians", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("horiuchi_2020_japan_politicians", source="conj")
@article{Horiuchi_2018, title={Identifying voter preferences for politicians’ personal attributes: a conjoint experiment in Japan}, volume={8}, ISSN={2049-8489}, url={http://dx.doi.org/10.1017/psrm.2018.26}, DOI={10.1017/psrm.2018.26}, number={1}, journal={Political Science Research and Methods}, publisher={Cambridge University Press (CUP)}, author={Horiuchi, Yusaku and Smith, Daniel M. and Yamamoto, Teppei}, year={2018}, month=Aug, pages={75–91} }
| 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 |