3,215 rated or chosen profiles from 3,215 respondents, in up to 1 tasks of 1 profile varying on 2 attributes.

About this table

Description	Respondents in Mexico, Guatemala, Chile and Uruguay (online panels) read one vignette about a new mayoral candidate randomized by candidate sex (man or woman) and by whether a corruption scandal involved the candidate's party, and rated their likelihood of voting, a second likelihood item, and trust. n = 3,215.
Reference	Elia, E. (2024). Can the fairer sex save the day? Voting for women after corruption scandals in Latin America. Political Behavior, 47, 97-117.
DOI	10.1007/s11109-024-09943-9
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/JAVSYJ

Notes

Conjoint table (irw_conjoint_2). rating_vote = likelihood of voting for the candidate, 1 very unlikely to 5 very likely; rating_tell = a second 5-point likelihood item (same scale; its wording is not in the deposit or the paper's open text); rating_trust = trust in the candidate, 1 none to 4 a lot ('No se' is missing); question wording is not in the deposit. Attribute levels are the authors' condition labels (Man/Woman, No scandal/Corruption scandal); the Spanish vignette text sits in the paper's SI. The 3,215 respondents are the authors' analysis sample.

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

Design

Topic	candidate choice, corruption accountability
A profile represents	person
Country	MX, GT, CL, UY
Language shown	es
Language of stored levels	en
Randomization restrictions	unknown
Task order	inferred from row order
Profile position	inferred from row order

Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.

Outcomes:

rating_tell	a second likelihood item stored as `tell` (wording not in deposit; unknown) [rating, 1 (muy improbable (very unlikely)) to 5 (muy probable (very likely))]
rating_trust	trust in the candidate (wording not in deposit; paraphrase) [rating, 1 (nada (not at all)) to 4 (mucho (a lot))]
rating_vote	likelihood of voting for the candidate (wording not in deposit; paraphrase) [rating, 1 (muy improbable (very unlikely)) to 5 (muy probable (very likely))]

Columns

attr_candidate_sexattr_scandalcov_age_groupcov_countrycov_duration_seccov_genderidprofilerating_tellrating_trustrating_votetask

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("elia_2024_scandal_women_candidates", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("elia_2024_scandal_women_candidates", source="conj")

How to cite

@article{Elia_2024, title={Can the Fairer Sex Save the Day? Voting for Women After Corruption Scandals in Latin America}, volume={47}, ISSN={1573-6687}, url={http://dx.doi.org/10.1007/s11109-024-09943-9}, DOI={10.1007/s11109-024-09943-9}, number={1}, journal={Political Behavior}, publisher={Springer Science and Business Media LLC}, author={Elia, Emily}, year={2024}, month=June, pages={97–117} }

Respondents	3,215
Rows (one per respondent, task and profile)	3,215
Tasks per respondent (most)	1
Profiles per task	1
Attributes	2

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

elia_2024_scandal_women_candidates