4,534 rated or chosen profiles from 772 respondents, in up to 3 tasks of 2 profiles varying on 4 attributes.

About this table

Description	US municipal government officials chose which of two hypothetical collaborative policy programs to adopt (up to 3 pairs; attributes: resource split, jobs created, proposing party, past collaborative experience). 772 officials, forced choice.
Reference	Liu, Y. (2024). Partisan collaboration in policy adoption: An experimental study with local government officials. Policy Studies Journal, 52(4), 955-967.
DOI	10.1111/psj.12551
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/CJKC4D

Notes

Conjoint table (irw_conjoint). choice is the official's pick of which of two collaborative programs to adopt (wording not deposited; paraphrase), forced choice, no opt-out. Attribute levels are the author's short labels (e.g. 750:250, Republicans), not the displayed wording. 43 of 772 officials answered fewer than 3 pairs (missing pairs absent, task numbers kept). City identifiers and city census figures dropped; party, gender, education and tenure kept as undocumented codes (_code).

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

Size and shape

Respondents	772
Rows (one per respondent, task and profile)	4,534
Tasks per respondent (most)	3
Profiles per task	2
Attributes	4
Tasks with no profile chosen	0

Design

Topic	public administration
A profile represents	policy
Country	US
Language shown	en
Language of stored levels	en
Randomization restrictions	unknown
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	Which of these two collaborative programs would you adopt? (paraphrase) [choice, forced]

Columns

attr_outcomeattr_partisanshipattr_resourceattr_trustchoicecov_agecov_education_codecov_electedcov_gender_codecov_ideologycov_party_id_codecov_racecov_tenure_codeidprofiletask

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 needed Browse on Redivisexplore and query Croissant 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("liu_2024_partisan_collaboration", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("liu_2024_partisan_collaboration", source="conj")

How to cite

@article{Liu_2024, title={Partisan collaboration in policy adoption: An experimental study with local government officials}, volume={52}, ISSN={1541-0072}, url={http://dx.doi.org/10.1111/psj.12551}, DOI={10.1111/psj.12551}, number={4}, journal={Policy Studies Journal}, publisher={Wiley}, author={Liu, Yixin}, year={2024}, month=July, pages={955–967} }

Version and provenance

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