14,400 rated or chosen profiles from 800 respondents, in up to 7,200 tasks of 2 profiles varying on 5 attributes.
| Description | Discrete choice experiment: 800 Italian households chose among a submarine canyon alternative, a cold-water coral alternative or neither, described by biodiversity, climate regulation, scientific value and price; 9 choice sets each. |
|---|---|
| Reference | Carlesi, L., Cubero Dudinskaya, E., Danovaro, R., D'Onghia, G., Mandolesi, S., Naspetti, S., & Zanoli, R. (2023). Estimating preferences for Mediterranean deep-sea ecosystem services: A discrete choice experiment. Marine Policy. |
| DOI | 10.1016/j.marpol.2023.105593 |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.17632/23tjmx64xc.2 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 800 |
|---|---|
| Rows (one per respondent, task and profile) | 14,400 |
| Tasks per respondent (most) | 7,200 |
| Profiles per task | 2 |
| Attributes | 5 |
| Tasks with no profile chosen | 3,264 |
| Topic | climate energy environment |
|---|---|
| A profile represents | place |
| Country | IT |
| Language shown | it |
| Language of stored levels | en |
| Randomization restrictions | unknown |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Outcomes:
| choice | unknown (choice among canyon, coral or none; wording not in the deposit) [choice, opt-out allowed] |
|---|
attr_biodiversityattr_climate_regulationattr_habitatattr_priceattr_scientific_valuechoicecov_agecov_areacov_educationcov_gendercov_household_incomeidprofiletasktrial_block
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("carlesi_2023_deep_sea", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("carlesi_2023_deep_sea", source="conj")
@article{Carlesi_2023, title={Estimating preferences for Mediterranean deep-sea ecosystem services: A discrete choice experiment}, volume={151}, ISSN={0308-597X}, url={http://dx.doi.org/10.1016/j.marpol.2023.105593}, DOI={10.1016/j.marpol.2023.105593}, journal={Marine Policy}, publisher={Elsevier BV}, author={Carlesi, Lorenzo and Cubero Dudinskaya, Emilia and Danovaro, Roberto and D’Onghia, Gianfranco and Mandolesi, Serena and Naspetti, Simona and Zanoli, Raffaele}, year={2023}, month=May, pages={105593} }
| IRW version | v584 |
|---|---|
| Redivis dataset | irw_conjoint_2 v1.4 |
| Redivis dataset DOI | 10.57761/hk97-wv30 |
| Manifest pin for this IRW version | v1.4 |
| Metadata source | irw_meta v44.0 |