beisermcgrath_2024_energy_policy

11,800 rated or chosen profiles from 1,180 respondents, in up to 5 tasks of 2 profiles varying on 5 attributes.

About this table

Description1,180 UK respondents (Lucid, August 2022) compared 5 pairs of hypothetical government energy policies varying energy source, funding, import restrictions, subsidies and support scheme, after a randomized framing message (control, economy, security). Outcomes: forced choice between the two policies and 1-5 support for each.
ReferenceBeiser-McGrath, L. F. (2024). Energy policy preferences in times of crisis: Evidence from survey experiments in the UK. Journal of Political Institutions and Political Economy, 5(4), 555-579.
DOI10.1561/113.00000111
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/RUNQHV

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

Respondents1,180
Rows (one per respondent, task and profile)11,800
Tasks per respondent (most)5
Profiles per task2
Attributes5
Tasks with no profile chosen0

Design

Topicclimate energy environment
A profile representspolicy
CountryGB
Language shownen
Language of stored levelsen
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceWhich policy do you prefer? [choice, forced]
ratingHow much do you support or oppose <Policy A>? [rating, 1 (Strongly Oppose) to 5 (Strongly Support)]

Columns

attr_energy_sourceattr_fundingattr_import_restrictionsattr_subsidiesattr_support_schemechoicecov_age_groupcov_brexit_votecov_educ_categcov_gendercov_nationcov_party_idcov_ukr_intervention_supportidprofileratingtasktrial_frame

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("beisermcgrath_2024_energy_policy", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("beisermcgrath_2024_energy_policy", source="conj")

How to cite

@article{Beiser_McGrath_2024, title={Energy Policy Preferences in Times of Crisis: Evidence from Survey Experiments in the UK}, volume={5}, ISSN={2689-4823}, url={http://dx.doi.org/10.1561/113.00000111}, DOI={10.1561/113.00000111}, number={4}, journal={Journal of Political Institutions and Political Economy}, publisher={Emerald}, author={Beiser-McGrath, Liam F.}, year={2024}, month=Dec, pages={555–579} }

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