guul_2021_caseworker_creaming

1,595 rated or chosen profiles from 1,595 respondents, in up to 1 tasks of 1 profile varying on 3 attributes.

About this table

Description1,595 caseworkers each read one client vignette that varied the client's competence (expert or novice), motivation (diligent or lazy) and gender (2x2x2) and rated their willingness to help on a 0-10 scale.
ReferenceGuul, T. S., Pedersen, M. J., & Petersen, N. B. G. (2021). Creaming among caseworkers: Effects of client competence and client motivation on caseworkers' willingness to help. Public Administration Review, 81(1).
DOI10.1111/puar.13297
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/ZUOSR8

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

Respondents1,595
Rows (one per respondent, task and profile)1,595
Tasks per respondent (most)1
Profiles per task1
Attributes3

Design

Topicpublic administration
A profile representsperson
Countryunknown
Language shownunknown
Language of stored levelsen
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

rating_willingnessunknown (willingness; variable label 'Willingness to help. 0-10. 10 = Very likely'; question wording not stored) [rating, 0 (Very unlikely) to 10 (Very likely)]

Columns

attr_client_genderattr_competenceattr_motivationidprofilerating_willingnesstasktrial_cluster

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("guul_2021_caseworker_creaming", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("guul_2021_caseworker_creaming", source="conj")

How to cite

@article{Guul_2020, title={Creaming among Caseworkers: Effects of Client Competence and Client Motivation on Caseworkers’ Willingness to Help}, volume={81}, ISSN={1540-6210}, url={http://dx.doi.org/10.1111/puar.13297}, DOI={10.1111/puar.13297}, number={1}, journal={Public Administration Review}, publisher={Wiley}, author={Guul, Thorbjørn Sejr and Pedersen, Mogens Jin and Petersen, Niels Bjørn Grund}, year={2020}, month=Oct, pages={12–22} }

Version and provenance

IRW versionv584
Redivis datasetirw_conjoint_2 v1.4
Redivis dataset DOI10.57761/hk97-wv30
Manifest pin for this IRW versionv1.4
Metadata sourceirw_meta v44.0