hoellerbauer_2023_civil_society_orgs

10,080 rated or chosen profiles from 2,527 respondents, in up to 2 tasks of 2 profiles varying on 4 attributes.

About this table

DescriptionMarket vendors in Malawi (face-to-face survey in 128 markets, n=2,527 with answers) compared 2 pairs of hypothetical civil society organizations varying where they were founded, the leader's former profession, funding source and party affiliation; they chose whose meetings they would attend and answered a second (scandal) question about the same pairs.
ReferenceHoellerbauer, S. (2023). Why join? How civil society organizations' attributes signal congruence and impact community engagement. Journal of Experimental Political Science, 10(1), 88-99.
DOI10.1017/XPS.2021.27
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/O6NMGE

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

Respondents2,527
Rows (one per respondent, task and profile)10,080
Tasks per respondent (most)2
Profiles per task2
Attributes4

Design

Topicother
A profile representsorganisation
CountryMW
Language shownunknown
Language of stored levelsen
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choice_meetingWhich organization's meetings would you be more willing to attend? (paraphrase) [choice, forced]
choice_scandalResponse to scandal question: Organization A or Organization B (paraphrase; wording unknown) [choice, forced]

Columns

attr_foundedattr_fundingattr_leader_former_professionattr_partychoice_meetingchoice_scandalcov_agecov_community_org_membercov_districtcov_educationcov_enumeratorcov_gendercov_hh_incomecov_intend_votecov_literatecov_marketcov_ngo_apathycov_registeredcov_sells_dailycov_servicecov_years_in_marketidprofiletask

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("hoellerbauer_2023_civil_society_orgs", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("hoellerbauer_2023_civil_society_orgs", source="conj")

How to cite

@article{Hoellerbauer_2021, title={Why Join? How Civil Society Organizations’ Attributes Signal Congruence and Impact Community Engagement}, volume={10}, ISSN={2052-2649}, url={http://dx.doi.org/10.1017/XPS.2021.27}, DOI={10.1017/xps.2021.27}, number={1}, journal={Journal of Experimental Political Science}, publisher={Cambridge University Press (CUP)}, author={Hoellerbauer, Simon}, year={2021}, month=Oct, pages={88–99} }

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