40,360 rated or chosen profiles from 2,018 respondents, in up to 10 tasks of 2 profiles varying on 7 attributes.

About this table

Description	Japanese voters (2019 online survey) chose between two hypothetical House of Councillors PR-list candidates in 10 paired tasks, the candidates varying age, gender, incumbency, other political experience, hometown, education and occupation. 2,018 respondents; forced choice; respondents had been randomized to four information/voting-rule arms beforehand.
Reference	Reeves, J. F., & Smith, D. M. (2026). Getting to know her: Information and gender bias in preferential voting systems. Electoral Studies, 103120.
DOI	10.1016/j.electstud.2026.103120
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/E4IQSC

Notes

Conjoint table (irw_conjoint). choice: "Which of the following two persons do you think is the most desirable as a proportional representation list member of the House of Councillors?" (Japanese instrument), forced choice, no opt-out. 10 tasks per respondent, 7 attributes, Japanese level text with the respondent's prefecture substituted into hometown and assembly levels; attribute order randomized per respondent (attrpos_*). trial_treatment_group = main-experiment arm (1-4). 2,018 respondents; Qualtrics IDs re-keyed; free-text answers not kept.

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	2,018
Rows (one per respondent, task and profile)	40,360
Tasks per respondent (most)	10
Profiles per task	2
Attributes	7
Tasks with no profile chosen	0

Design

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

Outcomes:

choice	次の2人の人物のうち、どちらがより比例代表選出の参議院議員として望ましいと思いますか。 [choice, forced]

Columns

attr_ageattr_educationattr_genderattr_hometownattr_incumbencyattr_occupationattr_politicsattrpos_ageattrpos_educationattrpos_genderattrpos_hometownattrpos_incumbencyattrpos_occupationattrpos_politicschoicecov_agecov_educationcov_gendercov_party_ididprofiletasktrial_treatment_group

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("reeves_2026_councillor_candidates", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("reeves_2026_councillor_candidates", source="conj")

How to cite

@article{Reeves_2026, title={Getting to know her: Information and gender bias in preferential voting systems}, volume={103}, ISSN={0261-3794}, url={http://dx.doi.org/10.1016/j.electstud.2026.103120}, DOI={10.1016/j.electstud.2026.103120}, journal={Electoral Studies}, publisher={Elsevier BV}, author={Reeves, Justin F. and Smith, Daniel M.}, year={2026}, month=Oct, pages={103120} }

Version and provenance

IRW version	v607
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 v47.0