darr_2026_solutions_news

8,424 rated or chosen profiles from 702 respondents, in up to 6 tasks of 2 profiles varying on 4 attributes.

One of 4 tables from the same source; this one is the solutions_news table. The others are listed under Related tables.

About this table

DescriptionUS Prolific respondents (n = 702, fielded 2024-08-09) chose between two news stories in each of 6 tasks in the solutions journalism conjoint, with randomized headline and editor's note (displayed text; the respondent's state piped into the headline is stored as [your state]), news outlet and photo (authors' description). One forced choice per task.
ReferenceDarr, J. P., & Robinson, S. (2026). Assessing civic journalism training: A mixed-method applied research study. Political Communication.
DOI10.1080/10584609.2026.2715698
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/WBOZEL

Notes

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Respondents702
Rows (one per respondent, task and profile)8,424
Tasks per respondent (most)6
Profiles per task2
Attributes4
Tasks with no profile chosen0

Design

Topicpolitical communication
A profile representsmessage
CountryUS
Language shownen
Language of stored levelsen
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceunknown [choice, forced]

Columns

attr_headlineattr_noteattr_photoattr_sourcechoicecov_birth_yearcov_duration_seccov_gendercov_hispcov_hisp_typecov_party_idcov_pid_codecov_pid_leancov_pid_strengthcov_pol_interestcov_racecov_residenceidprofiletasktrial_design_version

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.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

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("darr_2026_solutions_news", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("darr_2026_solutions_news", source="conj")

How to cite

@article{Darr_2026, title={Assessing Civic Journalism Training: A Mixed-Method Applied Research Study}, ISSN={1091-7675}, url={http://dx.doi.org/10.1080/10584609.2026.2715698}, DOI={10.1080/10584609.2026.2715698}, journal={Political Communication}, publisher={Informa UK Limited}, author={Darr, Joshua P. and Robinson, Sue}, year={2026}, month=Aug, pages={1–20} }

Version and provenance

IRW versionv584
Redivis datasetirw_conjoint v4.2
Redivis dataset DOI10.57761/qkye-q530
Manifest pin for this IRW versionv4.2
Metadata sourceirw_meta v44.0