7,048 rated or chosen profiles from 713 respondents, in up to 5 tasks of 2 profiles varying on 5 attributes.

One of 2 tables from the same source; this one is the students table. The others are listed under Related tables.

About this table

Description	Respondents from a US student sample (713) chose between two hypothetical candidates in five pairs varying gender, race, tax position, crime platform (prevention vs tough on crime) and education policy.
Reference	Laterzo-Tingley, I., & Christiani, L. (2024). The “tough-on-crime” left: Race, gender, and elections of law-and-order Democrats. Research & Politics, 11(3).
DOI	10.1177/20531680241261765
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/8ENZUC

Notes

Conjoint table (irw_conjoint). Paired candidate choice, five tasks, forced choice (exactly one chosen per pair); question wording not deposited. Pairs rebuilt from the stacked file; screen position and task order not recorded (profile numbering is outcome-independent but arbitrary). Level text = codebook labels, displayed wording undocumented. Student sample, 713 respondents.

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	713
Rows (one per respondent, task and profile)	7,048
Tasks per respondent (most)	5
Profiles per task	2
Attributes	5
Tasks with no profile chosen	0

Design

Topic	candidate choice
A profile represents	person
Country	US
Language shown	en
Language of stored levels	en
Randomization restrictions	unknown
Task order	unknown
Profile position	unknown

Outcomes:

choice	unknown [choice, forced]

Columns

attr_education_policyattr_genderattr_public_securityattr_raceattr_taxchoicecov_agecov_genderidprofiletask

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("laterzotingley_2024_crime_students", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("laterzotingley_2024_crime_students", source="conj")

How to cite

@article{Laterzo_Tingley_2024, title={The “tough-on-crime” left: Race, gender, and elections of law-and-order democrats}, volume={11}, ISSN={2053-1680}, url={http://dx.doi.org/10.1177/20531680241261765}, DOI={10.1177/20531680241261765}, number={3}, journal={Research &amp; Politics}, publisher={SAGE Publications}, author={Laterzo-Tingley, Isabel and Christiani, Leah}, year={2024}, month=July }

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