454 rated or chosen profiles from 454 respondents, in up to 1 tasks of 1 profile varying on 2 attributes.
One of 2 tables from the same source; this one is the compliance_scenario table. The others are listed under Related tables.
| Description | 454 people living near protected areas in Indonesia and Tanzania read one 2x2 scenario (peers' rule-breaking high/low, enforcer corruption high/low) and said how willing they were to follow protected-area rules (1 = very willing to 5 = very unwilling) and whether the rules are fair. |
|---|---|
| Reference | Ibbett, H., et al. (2024). Improving compliance around protected areas through fair administration of rules. Conservation Biology. |
| DOI | 10.1111/cobi.14332 |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.6084/m9.figshare.25772064 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 454 |
|---|---|
| Rows (one per respondent, task and profile) | 454 |
| Tasks per respondent (most) | 1 |
| Profiles per task | 1 |
| Attributes | 2 |
| Topic | climate energy environment |
|---|---|
| A profile represents | scenario |
| Country | ID, TZ |
| Language shown | unknown |
| Language of stored levels | en |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Outcomes:
| rating | How willing would you be to follow protected area rules? [rating, 1 (Very willing) to 5 (Very unwilling)] |
|---|---|
| rating_attitude | The rules of the protected area are fair and consistent with the law (agreement) [rating, 1 (Agree) to 5 (Disagree)] |
attr_enforcer_corruptionattr_peer_compliancecov_agecov_countrycov_education_yearscov_gendercov_mpiidprofileratingrating_attitudetask
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("ibbett_2024_pa_compliance_scenario", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("ibbett_2024_pa_compliance_scenario", source="conj")
@article{Ibbett_2024, title={Improving compliance around protected areas through fair administration of rules}, volume={39}, ISSN={1523-1739}, url={http://dx.doi.org/10.1111/cobi.14332}, DOI={10.1111/cobi.14332}, number={1}, journal={Conservation Biology}, publisher={Wiley}, author={Ibbett, Harriet and Dorward, Leejiah and Jones, Julia P. G. and Kohi, Edward M. and Dwiyahreni, Asri A. and Sankeni, Stephen and Prayitno, Karlina and Mchomvu, Jesca and Kaduma, Joseph and Saputra, Andie Wijaya and Agustin, Ika Yuni and Tryswidiarini, Tyassanti and Mawenya, Rose and Supriatna, Jatna and St John, Freya A. V.}, year={2024}, month=July }
| 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 |