42,100 rated or chosen profiles from 10,525 respondents, in up to 2 tasks of 2 profiles varying on 3 attributes.
| Description | German panel respondents (September 2021) compared two COVID-19 restriction proposals varying in stringency and universality (3G/2G/all citizens) under a shared pandemic-severity scenario. 10,525 respondents, 2 paired tasks; outcomes: preferred proposal, 0-10 rating of each, trust in government (one random proposal) and, for the unvaccinated, likelihood of vaccinating. |
|---|---|
| Reference | Hartmann, F., Humphreys, M., Geissler, F., Kluever, H., & Giesecke, J. (2023). Trading liberties: Estimating COVID-19 policy preferences from conjoint data. Political Analysis. |
| DOI | 10.1017/pan.2023.25 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/SVD9SV |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 10,525 |
|---|---|
| Rows (one per respondent, task and profile) | 42,100 |
| Tasks per respondent (most) | 2 |
| Profiles per task | 2 |
| Attributes | 3 |
| Tasks with no profile chosen | 0 |
| Topic | health covid |
|---|---|
| A profile represents | policy |
| Country | DE |
| Language shown | de |
| Language of stored levels | de |
| Randomization restrictions | yes (stated by the source): Severity is drawn once per task and shared by both proposals; the two proposals of a task are never the same vignette (stringency x universality cell), and the two tasks have different severities (all checked in the script). |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Because the attribute levels were not randomized independently, an estimator has to account for the restriction; a plain difference in means across levels can mislead.
Outcomes:
| choice | Which of the two proposals do you prefer? (paraphrase) [choice, forced] |
|---|---|
| rating | How do you rate this proposal? (paraphrase) [rating, 0 (definitiv dagegen) to 10 (definitiv dafür)] |
| rating_trust | How much would you trust the federal government if this proposal were implemented? (paraphrase) [rating, 0 (Überhaupt kein Vertrauen) to 10 (Volles Vertrauen)] |
| rating_vaccine | How likely would you be to get vaccinated against COVID-19 if this proposal were implemented? (paraphrase) [rating, 0 (Ich lasse mich mit Sicherheit nicht gegen Corona impfen) to 10 (Ich lasse mich mit Sicherheit gegen Corona impfen)] |
attr_severityattr_stringencyattr_universalitychoicecov_agecov_federal_statecov_gendercov_occupationcov_party_idcov_pr_error_exp7cov_samplecov_vaccinatedcov_vaccination_intentidprofileratingrating_trustrating_vaccinetask
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.
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("hartmann_2023_covid_restrictions", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("hartmann_2023_covid_restrictions", source="conj")
@article{Hartmann_2023, title={Trading Liberties: Estimating COVID-19 Policy Preferences from Conjoint Data}, volume={32}, ISSN={1476-4989}, url={http://dx.doi.org/10.1017/pan.2023.25}, DOI={10.1017/pan.2023.25}, number={2}, journal={Political Analysis}, publisher={Cambridge University Press (CUP)}, author={Hartmann, Felix and Humphreys, Macartan and Geissler, Ferdinand and Klüver, Heike and Giesecke, Johannes}, year={2023}, month=Aug, pages={285–293} }
| 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 |