1,101 rated or chosen profiles from 1,101 respondents, in up to 1 tasks of 1 profile varying on 3 attributes.

About this table

Description	1,101 German adults each saw one vignette about a migrant (reason for migration x religion x education, 24 cells) and said whether Germany should let him immigrate (yes/no).
Reference	Helbling, M. (2020). Attitudes towards climate change migrants. Climatic Change, 160(1), 89-102.
DOI	10.1007/s10584-020-02697-3
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/MJIVGR

Notes

Conjoint table (irw_conjoint_2). Single-vignette factorial survey: choice = yes to 'Do you think Germany should let him immigrate?' (single accept/reject, opt_out yes). Attribute levels are the codebook's short cell labels, not the vignette wording; id is the row number; one respondent without an answer dropped (1,101 of 1,102); the second vignette block (messages) is not included.

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	1,101
Rows (one per respondent, task and profile)	1,101
Tasks per respondent (most)	1
Profiles per task	1
Attributes	3
Tasks with no profile chosen	442

Design

Topic	immigration asylum
A profile represents	person
Country	DE
Language shown	unknown
Language of stored levels	en
Randomization restrictions	unknown
Task order	inferred from row order
Profile position	inferred from row order

Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.

Outcomes:

choice	Do you think Germany should let him immigrate? (1 Yes, 2 No; codebook) [choice, opt-out allowed]

Columns

attr_educationattr_reasonattr_religionchoicecov_birth_yearcov_educationcov_gendercov_left_rightidprofiletask

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("helbling_2020_climate_migrants", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("helbling_2020_climate_migrants", source="conj")

How to cite

@article{Helbling_2020, title={Attitudes towards climate change migrants}, volume={160}, ISSN={1573-1480}, url={http://dx.doi.org/10.1007/s10584-020-02697-3}, DOI={10.1007/s10584-020-02697-3}, number={1}, journal={Climatic Change}, publisher={Springer Science and Business Media LLC}, author={Helbling, Marc}, year={2020}, month=Mar, pages={89–102} }

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