carlesi_2023_deep_sea

14,400 rated or chosen profiles from 800 respondents, in up to 7,200 tasks of 2 profiles varying on 5 attributes.

About this table

DescriptionDiscrete 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.
ReferenceCarlesi, 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.
DOI10.1016/j.marpol.2023.105593
LicenceCC BY 4.0
Source datahttps://doi.org/10.17632/23tjmx64xc.2

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

Respondents800
Rows (one per respondent, task and profile)14,400
Tasks per respondent (most)7,200
Profiles per task2
Attributes5
Tasks with no profile chosen3,264

Design

Topicclimate energy environment
A profile representsplace
CountryIT
Language shownit
Language of stored levelsen
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceunknown (choice among canyon, coral or none; wording not in the deposit) [choice, opt-out allowed]

Columns

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.

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("carlesi_2023_deep_sea", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("carlesi_2023_deep_sea", source="conj")

How to cite

@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} }

Version and provenance

IRW versionv584
Redivis datasetirw_conjoint_2 v1.4
Redivis dataset DOI10.57761/hk97-wv30
Manifest pin for this IRW versionv1.4
Metadata sourceirw_meta v44.0