988 rated or chosen profiles from 988 respondents, in up to 1 tasks of 1 profile varying on 2 attributes.

About this table

Description	988 German Green voters (respondi panel, Sept 2021) read a description of the Campact strategic-voting campaign for the SPD candidate in district 196, with two clauses randomly added (justification of the campaign; the Greens' support for it; 2x2, between-subject). Outcome: evaluation of the campaign, 1 (very bad) to 10 (very good).
Reference	Jankowski, M. (2023). Support for strategic voting campaigns: Evidence from a survey experiment during the German General Election of 2021. Politische Vierteljahresschrift.
Licence	CC0 1.0
Source data	https://doi.org/10.7910/dvn/qnomxj

Notes

Conjoint table (irw_conjoint_2). Factorial vignette (Germany, 2021): one description of Campact's strategic-voting campaign per respondent, with a justification clause and a Green-party-support clause each randomly shown or '(not shown)' (2x2); rating = evaluation of the campaign, 1 'very bad' to 10 'very good'. Level text translated from the author's preprint. Both factors describe the evaluated campaign, so it is included although the author calls them treatments.

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Design

Topic	democracy institutions
A profile represents	policy
Country	DE
Language shown	de
Language of stored levels	en
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

rating	How do you evaluate this campaign of Campact for the SPD candidate in electoral district 196? (translated) [rating, 1 (very bad) to 10 (very good)]

Columns

attr_justificationattr_party_cuecov_agecov_gendercov_left_rightidprofileratingtask

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("jankowski_2023_strategic_voting", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("jankowski_2023_strategic_voting", source="conj")

How to cite

@misc{https://doi.org/10.7910/dvn/qnomxj,
  doi = {10.7910/DVN/QNOMXJ},
  url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/QNOMXJ},
  author = {Jankowski, Michael},
  keywords = {Social Sciences},
  title = {Replication Data for: Support for strategic voting campaigns: Evidence from a survey experiment during the German General Election of 2021},
  publisher = {Harvard Dataverse},
  year = {2023}
}

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

Respondents	988
Rows (one per respondent, task and profile)	988
Tasks per respondent (most)	1
Profiles per task	1
Attributes	2

jankowski_2023_strategic_voting