fesenfeld_2023_climate_legit_us

10,474 rated or chosen profiles from 1,350 respondents, in up to 4 tasks of 2 profiles varying on 6 attributes.

One of 2 tables from the same source; this one is the us table. The others are listed under Related tables.

About this table

DescriptionUS respondents (online survey, December 2020; 1,350 of the 1,360 respondents) compared two climate-policy packages in four rounds, packages varying government support, public investment, producer standards, CO2 tax, restrictions and the proposing body (federal government, expert commission, citizens' assembly). Outcomes: forced choice and a 1-7 rating of each package.
ReferenceFesenfeld, L. P., Freudlsperger, C., Kuntze, L., & Ingold, K. (2023). Replication data for: Legitimizing climate action in democracies [Data set]. Harvard Dataverse.
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/WQW6EK

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

Respondents1,350
Rows (one per respondent, task and profile)10,474
Tasks per respondent (most)4
Profiles per task2
Attributes6
Tasks with no profile chosen0

Design

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

Outcomes:

choiceWhich policy package do you prefer? [choice, forced]
ratingunknown [rating, 1 (unknown) to 7 (unknown)]

Columns

attr_investmentsattr_proposerattr_restrictionsattr_standardsattr_supportattr_taxchoicecov_agecov_education_codecov_gender_codecov_left_rightidprofileratingtask

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("fesenfeld_2023_climate_legit_us", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("fesenfeld_2023_climate_legit_us", source="conj")

How to cite

@misc{https://doi.org/10.7910/dvn/wqw6ek,
  doi = {10.7910/DVN/WQW6EK},
  url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/WQW6EK},
  author = {Fesenfeld, Lukas Paul and Freudlsperger, Christian and Kuntze, Lennart and Ingold, Karin},
  keywords = {Social Sciences, Conjoint Experiment, Perceived Legitimacy},
  title = {Replication Data for: Legitimizing climate action in democracies},
  publisher = {Harvard Dataverse},
  year = {2023}
}

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