12,330 rated or chosen profiles from 1,233 respondents, in up to 5 tasks of 2 profiles varying on 10 attributes.
| Description | US MTurk respondents (spring 2019, second wave of a panel; 1,233 respondents) compared five pairs of hypothetical cyberattacks varying target, damage, deaths, perpetrator type and origin, purpose, intelligence-community confidence, expert agreement and a policymaker's reaction. Outcomes per scenario: 0-100 confidence in the attribution, 0-100 severity, and support (1/0) for six foreign-policy responses or six criminal sentences. |
|---|---|
| Reference | Leal, M., & Musgrave, P. (2022). Cheerleading in cyberspace: How the American public judges attribution claims for cyberattacks. Foreign Policy Analysis, 18(2), orac003. |
| DOI | 10.1093/fpa/orac003 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/MVDDKX |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 1,233 |
|---|---|
| Rows (one per respondent, task and profile) | 12,330 |
| Tasks per respondent (most) | 5 |
| Profiles per task | 2 |
| Attributes | 10 |
| Topic | foreign policy security |
|---|---|
| A profile represents | scenario |
| Country | US |
| Language shown | en |
| Language of stored levels | en |
| Randomization restrictions | observed in the data (not documented by the source): No source documents a rule, but 'A group of terrorists' occurs only with origin Iran, Russia or the United States and 'A government agency' never with the United States. Level shares unequal for several attributes (e.g. economic damage 'Minimal' 1,795 vs 'Billions of dollars' 4,057 raw profiles; perpetrator type 'A group of terrorists' rarest). |
| Task order | recorded in the deposit |
| 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.
Outcomes:
| rating_confidence | On a scale of 0 to 100, where 0 indicates no confidence and 100 indicates complete confidence, how confident are you in the intelligence community's assessment of who was responsible for each attack? [rating, 0 (Not confident) to 100 (Complete confidence)] |
|---|---|
| rating_severity | On a scale of 0 to 100, where 0 indicates "not severe" and 100 indicates "very severe", how would you rate the severity of each event? [rating, 0 (Not severe) to 100 (Very severe)] |
| rating_support_airstrikes | For scenario 1 [2] only: Would you approve of each of the following measures against those the intelligence community believes to be the perpetrators? You may support any, all, or none of these responses. Conduct airstrikes [rating, 0 (Would not support) to 1 (Would support)] |
| rating_support_community_service | For scenario 1 [2] only: Would you approve of each of the following sentences for those believed to be the perpetrators? You may support any, all, or none of these responses. Community service [rating, 0 (Would not support) to 1 (Would support)] |
| rating_support_cyberattacks | For scenario 1 [2] only: Would you approve of each of the following measures against those the intelligence community believes to be the perpetrators? You may support any, all, or none of these responses. Conduct cyberattacks [rating, 0 (Would not support) to 1 (Would support)] |
| rating_support_cyberespionage | For scenario 1 [2] only: Would you approve of each of the following measures against those the intelligence community believes to be the perpetrators? You may support any, all, or none of these responses. Intensify cyberespionage [rating, 0 (Would not support) to 1 (Would support)] |
| rating_support_death_penalty | For scenario 1 [2] only: Would you approve of each of the following sentences for those believed to be the perpetrators? You may support any, all, or none of these responses. The death penalty [rating, 0 (Would not support) to 1 (Would support)] |
| rating_support_diplomatic | For scenario 1 [2] only: Would you approve of each of the following measures against those the intelligence community believes to be the perpetrators? You may support any, all, or none of these responses. Condemn through diplomatic channels [rating, 0 (Would not support) to 1 (Would support)] |
| rating_support_do_nothing | For scenario 1 [2] only: Would you approve of each of the following measures against those the intelligence community believes to be the perpetrators? You may support any, all, or none of these responses. Do nothing [rating, 0 (Would not support) to 1 (Would support)] |
| rating_support_fine | For scenario 1 [2] only: Would you approve of each of the following sentences for those believed to be the perpetrators? You may support any, all, or none of these responses. A large monetary fine [rating, 0 (Would not support) to 1 (Would support)] |
| rating_support_life_sentence | For scenario 1 [2] only: Would you approve of each of the following sentences for those believed to be the perpetrators? You may support any, all, or none of these responses. A life sentence [rating, 0 (Would not support) to 1 (Would support)] |
| rating_support_no_punishment | For scenario 1 [2] only: Would you approve of each of the following sentences for those believed to be the perpetrators? You may support any, all, or none of these responses. No punishment [rating, 0 (Would not support) to 1 (Would support)] |
| rating_support_prison | For scenario 1 [2] only: Would you approve of each of the following sentences for those believed to be the perpetrators? You may support any, all, or none of these responses. A short prison term [rating, 0 (Would not support) to 1 (Would support)] |
| rating_support_sanctions | For scenario 1 [2] only: Would you approve of each of the following measures against those the intelligence community believes to be the perpetrators? You may support any, all, or none of these responses. Apply economic sanctions [rating, 0 (Would not support) to 1 (Would support)] |
attr_deathsattr_economic_damageattr_expertsattr_intel_confidenceattr_perpetrator_originattr_perpetrator_typeattr_policymakerattr_policymaker_reactionattr_purposeattr_targetcov_birth_yearcov_cyber_foreigncov_cyber_preparedcov_cyber_uscov_cyber_worriedcov_duration_seccov_educationcov_gendercov_hispaniccov_ideologycov_ideology_forcedcov_party_idcov_party_leancov_party_strengthcov_racecov_therm_brazilcov_therm_canadacov_therm_chinacov_therm_francecov_therm_germanycov_therm_indiacov_therm_irancov_therm_israelcov_therm_japancov_therm_north_koreacov_therm_russiacov_therm_south_koreacov_therm_ukcov_therm_usacov_therm_vietnamcov_threat_china_disputescov_threat_china_militarycov_threat_climatecov_threat_cybercov_threat_epidemicscov_threat_financial_crisiscov_threat_immigrantscov_threat_iran_nuclearcov_threat_islamic_fundamentalismcov_threat_nk_nuclearcov_threat_russiacov_threat_terrorismidprofilerating_confidencerating_severityrating_support_airstrikesrating_support_community_servicerating_support_cyberattacksrating_support_cyberespionagerating_support_death_penaltyrating_support_diplomaticrating_support_do_nothingrating_support_finerating_support_life_sentencerating_support_no_punishmentrating_support_prisonrating_support_sanctionstask
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("leal_2022_cyber_attribution", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("leal_2022_cyber_attribution", source="conj")
@article{Leal_2022, title={Cheerleading in Cyberspace: How the American Public Judges Attribution Claims for Cyberattacks}, volume={18}, ISSN={1743-8594}, url={http://dx.doi.org/10.1093/fpa/orac003}, DOI={10.1093/fpa/orac003}, number={2}, journal={Foreign Policy Analysis}, publisher={Oxford University Press (OUP)}, author={Leal, Marcelo and Musgrave, Paul}, year={2022}, month=Mar }
| 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 |