1,246 rated or chosen profiles from 1,246 respondents, in up to 1 tasks of 1 profile varying on 4 attributes.
One of 2 tables from the same source; this one is the democratic_peace table. The others are listed under Related tables.
| Description | 1,246 US MTurk respondents (June 2016) each read one vignette about a country developing nuclear weapons with randomized alliance, democracy, trade and stated motives (a Tomz & Weeks 2013 replication plus a threat-information arm); outcome is a 5-point favor/oppose rating of a US strike on its nuclear sites. |
|---|---|
| Reference | Acharya, A., Blackwell, M., & Sen, M. (2018). Analyzing causal mechanisms in survey experiments. Political Analysis, 26(4), 357-378. |
| DOI | 10.1017/pan.2018.19 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/KHE44F |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 1,246 |
|---|---|
| Rows (one per respondent, task and profile) | 1,246 |
| Tasks per respondent (most) | 1 |
| Profiles per task | 1 |
| Attributes | 4 |
| Topic | foreign policy security |
|---|---|
| A profile represents | scenario |
| Country | US |
| Language shown | en |
| Language of stored levels | en |
| Task order | recorded in the deposit |
| Profile position | inferred from row order |
Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.
Outcomes:
| rating | Would you favor or oppose using the U.S. military to attack the country's nuclear development sites? [rating, 1 (Favor strongly) to 5 (Oppose strongly)] |
|---|
attr_allianceattr_democracyattr_motivesattr_tradecov_agecov_dem_place_codecov_education_codecov_employed_codecov_follow_gov_codecov_gender_codecov_pid_codecov_pid_lean_codecov_pid_strong_dem_codecov_pid_strong_rep_codecov_race_codecov_rep_place_codeidprofileratingtask
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("acharya_2018_democratic_peace", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("acharya_2018_democratic_peace", source="conj")
@article{Acharya_2018, title={Analyzing Causal Mechanisms in Survey Experiments}, volume={26}, ISSN={1476-4989}, url={http://dx.doi.org/10.1017/pan.2018.19}, DOI={10.1017/pan.2018.19}, number={4}, journal={Political Analysis}, publisher={Cambridge University Press (CUP)}, author={Acharya, Avidit and Blackwell, Matthew and Sen, Maya}, year={2018}, month=Aug, pages={357–378} }
| 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 |