gidengil_2022_executive_aggrandizement

33,654 rated or chosen profiles from 2,830 respondents, in up to 6 tasks of 2 profiles varying on 7 attributes.

About this table

DescriptionCanadian respondents outside Quebec (Dynata, May 2020, n=2,830 before the article's exclusions) chose between two parliamentary candidates in 6 tasks varying gender, age, experience, party, COVID-19 economic-aid and lockdown positions, and a randomized executive-aggrandizement position (Parliament or courts version by respondent).
ReferenceGidengil, E., Stolle, D., & Bergeron-Boutin, O. (2022). COVID-19 and support for executive aggrandizement. Canadian Journal of Political Science, 55(2), 342-372.
DOI10.1017/S0008423922000117
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/7TMQQN

Notes

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

Size and shape

Respondents2,830
Rows (one per respondent, task and profile)33,654
Tasks per respondent (most)6
Profiles per task2
Attributes7
Tasks with no profile chosen0

Design

Topiccandidate choice
A profile representsperson
CountryCA
Language shownen
Language of stored levelsen
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceSelect your preferred candidate in each pair (paraphrase) [choice, forced]

Columns

attr_ageattr_aidattr_checksattr_experienceattr_genderattr_lockdownattr_partyattrpos_aidattrpos_checksattrpos_lockdownchoicecov_anxious_activitiescov_anxious_thinkcov_attention_passcov_birth_yearcov_canadian_citizencov_duration_seccov_educationcov_gendercov_lockdown_preferencecov_party_idcov_provinceidprofiletasktrial_checks_versiontrial_task_time_sec

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 neededBrowse on Redivisexplore and queryCroissant 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("gidengil_2022_executive_aggrandizement", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("gidengil_2022_executive_aggrandizement", source="conj")

How to cite

@article{Gidengil_2022, title={COVID-19 and Support for Executive Aggrandizement}, volume={55}, ISSN={1744-9324}, url={http://dx.doi.org/10.1017/S0008423922000117}, DOI={10.1017/s0008423922000117}, number={2}, journal={Canadian Journal of Political Science}, publisher={Cambridge University Press (CUP)}, author={Gidengil, Elisabeth and Stolle, Dietlind and Bergeron-Boutin, Olivier}, year={2022}, month=Apr, pages={342–372} }

Version and provenance

IRW versionv584
Redivis datasetirw_conjoint v4.2
Redivis dataset DOI10.57761/qkye-q530
Manifest pin for this IRW versionv4.2
Metadata sourceirw_meta v44.0