11,606 rated or chosen profiles from 829 respondents, in up to 7 tasks of 2 profiles varying on 7 attributes.

About this table

Description	Finnish respondents (Qualtrics panel, May 2019, n=829) chose between two hypothetical European Parliament candidates in 7 pairs varying gender, ideology, issue focus, experience, representative focus, citizenship and EU-integration position, each attribute possibly not shown.
Reference	Christensen, H. S., La Rosa, M. S., & Grönlund, K. (2020). How candidate characteristics affect favorability in European Parliament elections: Evidence from a conjoint experiment in Finland. European Union Politics, 21(4), 563-583.
DOI	10.1177/1465116520929765
Licence	CC BY 4.0
Source data	https://doi.org/10.17605/osf.io/w6jkq

Notes

Conjoint table (irw_conjoint). choice = which of two candidates the respondent would be most likely to vote for in the EP elections (paraphrase), forced, no opt-out. Every attribute had a randomized 'not shown' level (stored as (not shown)); no restrictions. Levels are the article's English text; covariates limited to EU interest and the authors' 3-category ideology.

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	829
Rows (one per respondent, task and profile)	11,606
Tasks per respondent (most)	7
Profiles per task	2
Attributes	7
Tasks with no profile chosen	0

Design

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

Outcomes:

choice	Which of these two candidates would you be most likely to vote for in the elections? (paraphrase) [choice, forced]

Columns

attr_citizenshipattr_eu_integrationattr_experienceattr_genderattr_ideologyattr_issueattr_representationchoicecov_eu_interestcov_ideology_3catidprofiletask

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("christensen_2020_ep_candidates", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("christensen_2020_ep_candidates", source="conj")

How to cite

@article{Christensen_2020, title={How candidate characteristics affect favorability in European Parliament elections: Evidence from a conjoint experiment in Finland}, volume={21}, ISSN={1741-2757}, url={http://dx.doi.org/10.1177/1465116520929765}, DOI={10.1177/1465116520929765}, number={3}, journal={European Union Politics}, publisher={SAGE Publications}, author={Christensen, Henrik S and Rosa, Marco S La and Grönlund, Kimmo}, year={2020}, month=June, pages={519–540} }

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