48,922 rated or chosen profiles from 4,973 respondents, in up to 5 tasks of 2 profiles varying on 5 attributes.
| Description | Swiss car holders (non-EV and EV holders in four German-speaking cantons, 2018, n = 5,325 completes, 4,973 with outcomes) chose between and rated two electric-vehicle policy packages varying charging infrastructure, purchase subsidy, information requirements, registration ban and (for a random half) funding, in 5 paired tasks. |
|---|---|
| Reference | Brückmann, G., & Bernauer, T. (2020). What drives public support for policies to enhance electric vehicle adoption? Environmental Research Letters, 15(9), 094002. |
| DOI | 10.1088/1748-9326/ab90a5 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/XF0GD9 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 4,973 |
|---|---|
| Rows (one per respondent, task and profile) | 48,922 |
| Tasks per respondent (most) | 5 |
| Profiles per task | 2 |
| Attributes | 5 |
| Tasks with no profile chosen | 0 |
| Topic | climate energy environment |
|---|---|
| A profile represents | policy |
| Country | CH |
| Language shown | de |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): Article Table 1 note b: funding 'No additional funding' was shown if and only if the proposal had no new charging infrastructure and no subsidy (funding arm only); otherwise fully randomized. Funding attribute shown only to the randomly assigned half (visiblefin). |
| Task order | inferred from row order |
| 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. Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.
Outcomes:
| choice | Which of the two policy proposals do you prefer? (paraphrase) [choice, forced] |
|---|---|
| rating | Rating of each policy proposal on a 7-point Likert scale (paraphrase) [rating, 1 (unknown) to 7 (unknown)] |
attr_chargingattr_fundingattr_informationattr_registrationattr_subsidychoicecov_age_groupcov_carusedayscov_ev_holdercov_ever_drove_bevcov_gender_codecov_higher_educationcov_household_incomecov_icev_backupcov_nextcarbev_codecov_printcov_teslaidprofileratingtasktrial_funding_shown
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("bruckmann_2020_ev_policy", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("bruckmann_2020_ev_policy", source="conj")
@article{Br_ckmann_2020, title={What drives public support for policies to enhance electric vehicle adoption?}, volume={15}, ISSN={1748-9326}, url={http://dx.doi.org/10.1088/1748-9326/ab90a5}, DOI={10.1088/1748-9326/ab90a5}, number={9}, journal={Environmental Research Letters}, publisher={IOP Publishing}, author={Brückmann, Gracia and Bernauer, Thomas}, year={2020}, month=Aug, pages={094002} }
| 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 |