9,666 rated or chosen profiles from 1,611 respondents, in up to 3 tasks of 2 profiles varying on 9 attributes.

One of 2 tables from the same source; this one is the elect_politician table. The others are listed under Related tables.

About this table

Description	Japanese adults (Research Now online panel, 2015, n = 1,611) chose between pairs of hypothetical House of Representatives candidates in 3 tasks varying gender, party, consumption-tax stance, policy priority, age, experience, education, children and marital status, and rated each candidate 1-7.
Reference	Kage, R., Rosenbluth, F. M., & Tanaka, S. (2019). What explains low female political representation? Evidence from survey experiments in Japan. Politics & Gender, 15(2), 285-309.
DOI	10.1017/S1743923X18000223
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/9ZOYJN

Notes

Conjoint table (irw_conjoint). choice = which of the two candidates the respondent would personally prefer to see elected to the House of Representatives (forced choice, no opt-out); rating = an evaluation of each candidate, 1-7, wording and anchors not documented (stored raw). 9 attributes, author English labels; attribute order randomized per task, not recorded. Weight kept; N 1,611 as in the article.

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Respondents	1,611
Rows (one per respondent, task and profile)	9,666
Tasks per respondent (most)	3
Profiles per task	2
Attributes	9
Tasks with no profile chosen	0

Design

Topic	candidate choice
A profile represents	person
Country	JP
Language shown	ja
Language of stored levels	en
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	For each pair of candidates, please indicate which of the two candidates you would personally prefer. This exercise is purely hypothetical. But even if you are not entirely sure, please indicate which of the two you prefer. [choice, forced]
rating	unknown [rating, 1 (unknown) to 7 (unknown)]

Columns

attr_ageattr_childrenattr_consumption_taxattr_educationattr_experienceattr_genderattr_marital_statusattr_partyattr_policy_prioritychoicecov_age_groupcov_educationcov_employmentcov_gendercov_maritalcov_party_idcov_survey_weightidprofileratingtask

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.

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant metadataHugging Face, Kaggle, OpenML

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("kage_2019_elect_politician", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("kage_2019_elect_politician", source="conj")

How to cite

@article{Kage_2018, title={What Explains Low Female Political Representation? Evidence from Survey Experiments in Japan}, volume={15}, ISSN={1743-9248}, url={http://dx.doi.org/10.1017/S1743923X18000223}, DOI={10.1017/s1743923x18000223}, number={2}, journal={Politics &amp; Gender}, publisher={Cambridge University Press (CUP)}, author={Kage, Rieko and Rosenbluth, Frances M. and Tanaka, Seiki}, year={2018}, month=July, pages={285–309} }

Version and provenance

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