4,808 rated or chosen profiles from 1,628 respondents, in up to 3 tasks of 1 profile varying on 5 attributes.
| Description | UK YouGov panel respondents (n = 1,676; three tasks each, 4,808 task rows with an answer) read short speech excerpts by a fictional MP randomized by argument style (emotion, aggression, evidence), the style's treatment status, policy area and MP name (gender), and rated the MP's likeability and competence and how emotional, aggressive or evidence-based the argument was. |
|---|---|
| Reference | Hargrave, L. (2023). A double standard? Gender bias in voters' perceptions of political arguments. British Journal of Political Science. |
| DOI | 10.1017/S0007123422000515 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/LZ4NKW |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 1,628 |
|---|---|
| Rows (one per respondent, task and profile) | 4,808 |
| Tasks per respondent (most) | 3 |
| Profiles per task | 1 |
| Attributes | 5 |
| Topic | political communication |
|---|---|
| A profile represents | message |
| Country | GB |
| Language shown | en |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): styles and policies sampled without replacement across a respondent's three tasks (manuscript Research Design); style status and MP name sampled with replacement; the style perception question asked only for the style shown |
| Task order | recorded in the deposit |
| Profile position | inferred from row order |
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_competent | Do you agree or disagree that the MP seems competent? (paraphrase of the manuscript's quote) [rating, 1 (strongly disagree) to 5 (strongly agree)] |
|---|---|
| rating_likeable | Do you agree or disagree that the MP seems likeable? (paraphrase of the manuscript's quote) [rating, 1 (strongly disagree) to 5 (strongly agree)] |
| rating_perceived_aggressive | Do you agree or disagree that the argument made by [MP name] is aggressive? (asked only when the style was aggression; paraphrase) [rating, 1 (strongly disagree) to 5 (strongly agree)] |
| rating_perceived_emotional | Do you agree or disagree that the argument made by [MP name] is emotional? (asked only when the style was emotion) [rating, 1 (strongly disagree) to 5 (strongly agree)] |
| rating_perceived_evidence | Do you agree or disagree that the argument made by [MP name] is evidence-based? (asked only when the style was evidence) [rating, 1 (strongly disagree) to 5 (strongly agree)] |
attr_mp_first_nameattr_mp_surnameattr_policy_areaattr_styleattr_style_versioncov_agecov_gendercov_survey_weightidprofilerating_competentrating_likeablerating_perceived_aggressiverating_perceived_emotionalrating_perceived_evidencetasktrial_speech_number
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("hargrave_2023_speech_style", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("hargrave_2023_speech_style", source="conj")
@article{Hargrave_2022, title={A Double Standard? Gender Bias in Voters’ Perceptions of Political Arguments}, volume={53}, ISSN={1469-2112}, url={http://dx.doi.org/10.1017/S0007123422000515}, DOI={10.1017/s0007123422000515}, number={2}, journal={British Journal of Political Science}, publisher={Cambridge University Press (CUP)}, author={Hargrave, Lotte}, year={2022}, month=Nov, pages={327–345} }
| 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 |