3,566 rated or chosen profiles from 446 respondents, in up to 8 tasks of 1 profile varying on 4 attributes.
One of 2 tables from the same source; this one is the sanction_fairness table. The others are listed under Related tables.
| Description | 446 people living near protected areas in Indonesia (229) and Tanzania (217) each rated how fair 8 vignettes of a law-enforcement sanction were; crime, offender origin, offender power and sanction (incl. a bribe) varied in a full 4x2x2x2 factorial. Fairness rated Unfair/Neutral/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 | 446 |
|---|---|
| Rows (one per respondent, task and profile) | 3,566 |
| Tasks per respondent (most) | 8 |
| Profiles per task | 1 |
| Attributes | 4 |
| Topic | crime justice policing |
|---|---|
| A profile represents | scenario |
| Country | ID, TZ |
| Language shown | unknown |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): crime levels are country-specific: logging only in Indonesia, grazing only in Tanzania, hunting in both with different wording; within a country the 32 vignettes are a full factorial, each respondent sees one fixed block of 8 |
| 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:
| rating | How fair was the sanction distributed by the enforcer in this vignette (perceived fairness; exact wording not in deposit or article text read) (paraphrase) [rating, 1 (Unfair) to 3 (Fair)] |
|---|
attr_crimeattr_offender_originattr_offender_powerattr_sanctioncov_agecov_countrycov_gendercov_mpiidprofileratingtasktrial_vignette_id
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_sanction_fairness", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("ibbett_2024_pa_sanction_fairness", 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 |