2,400 rated or chosen profiles from 240 respondents, in up to 5 tasks of 2 profiles varying on 3 attributes.

About this table

Description	Paired conjoint: 240 respondents chose between two hypothetical first-year seminars described by theme, professor's last name and professor's pronouns; 5 tasks each, forced choice.
Reference	Liu, L.-Y., Brough, C., & Gaozhao, D. (2026). Before the first lecture: The effects of instructor identity on course enrollment. PS: Political Science & Politics, 59(4), 690-696.
DOI	10.1017/S1049096526102054
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/DSOBOP

Notes

Conjoint table (irw_conjoint_2). Paired choice between two hypothetical first-year seminars described by theme, professor's last name and professor's pronouns (she/her/her, he/his/him, they/their/theirs); exactly one seminar is chosen per task, no opt-out. The question wording is not in the deposit; 240 respondents, 5 tasks each; attributes independently randomized.

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	240
Rows (one per respondent, task and profile)	2,400
Tasks per respondent (most)	5
Profiles per task	2
Attributes	3
Tasks with no profile chosen	0

Design

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

Outcomes:

choice	unknown (ChosenItem: which of the two seminars the respondent chose) [choice, forced]

Columns

attr_professor_nameattr_professor_pronounsattr_themechoiceidprofiletask

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_2026_instructor_identity", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("liu_2026_instructor_identity", source="conj")

How to cite

@article{Liu_2026, title={Before the First Lecture: The Effects of Instructor Identity on Course Enrollment}, volume={59}, ISSN={1537-5935}, url={http://dx.doi.org/10.1017/S1049096526102054}, DOI={10.1017/s1049096526102054}, number={4}, journal={PS: Political Science &amp; Politics}, publisher={Cambridge University Press (CUP)}, author={Liu, Li-Yin and Brough, Christopher and Gaozhao, Dongfang}, year={2026}, month=Apr, pages={690–696} }

Version and provenance

IRW version	v584
Redivis dataset	irw_conjoint_2 v1.4
Redivis dataset DOI	10.57761/hk97-wv30
Manifest pin for this IRW version	v1.4
Metadata source	irw_meta v44.0