4,630 rated or chosen profiles from 386 respondents, in up to 6 tasks of 2 profiles varying on 6 attributes.

About this table

Description	Respondents (n=386) chose which of two hypothetical art-museum visits they preferred, varying featured artist, exhibition description, event, program, ticket cost and location; 6 pairs each.
Reference	Mohr, Z., Olivares, A., & Piatak, J. (2024). Are public spaces welcoming to all? A conjoint experiment on cultural representation and inclusionary practices in museums. Public Administration, 102(3), 841-859.
DOI	10.1111/padm.12953
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/IPRSTZ

Notes

Conjoint table (irw_conjoint). choice = which of two museums (Museum A / Museum B) the respondent chose; forced choice, verbatim wording not deposited. Attribute levels are the authors' short Stata value labels, not necessarily the displayed text; cost is the stored dollar amount (the .dta cost labels are mis-attached). Randomization restrictions not documented, level shares uniform. One unanswered task dropped (2,315 tasks).

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	386
Rows (one per respondent, task and profile)	4,630
Tasks per respondent (most)	6
Profiles per task	2
Attributes	6
Tasks with no profile chosen	0

Design

Topic	consumer products
A profile represents	product service
Country	unknown
Language shown	unknown
Language of stored levels	en
Randomization restrictions	unknown
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	Which museum would you choose, Museum A or Museum B? (paraphrase) [choice, forced]

Columns

attr_artistattr_costattr_descriptionattr_eventattr_locationattr_programchoicecov_gendercov_generationcov_museum_last12mcov_party_idcov_racecov_visited_mintidprofiletask

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("mohr_2024_museum_visits", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("mohr_2024_museum_visits", source="conj")

How to cite

@article{Mohr_2023, title={Are public spaces welcoming to all? A conjoint experiment on cultural representation and inclusionary practices in museums}, volume={102}, ISSN={1467-9299}, url={http://dx.doi.org/10.1111/padm.12953}, DOI={10.1111/padm.12953}, number={3}, journal={Public Administration}, publisher={Wiley}, author={Mohr, Zachary and Olivares, Alexandra and Piatak, Jaclyn}, year={2023}, month=Aug, pages={841–859} }

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