issueirt_votes_shin_2024

303,921 responses from 697 respondents to 373 items.

About this table

DescriptionRoll-call vote responses from the 52nd to 54th U.S. House of Representatives.
ReferenceShin, S. (2024). Measuring Issue Specific Ideal Points from Roll Call Votes (Doctoral dissertation, Ph. D. thesis, Harvard University. Ph. D. Candidate, Department of Government and Institute for Quantitative Social Science).
LicenceGPL-3.0
Source datahttps://github.com/sooahnshin/issueirt/blob/main/data/us1890s_votes.rda

Size and shape

Responses303,921
Respondents697
Items373
Response categories2
Responses per respondent436.042
Responses per item814.802
Density1.169
LongitudinalTRUE

Classification

age rangeAdult (18+)
sampleTargeted/specific
construct typeBehavioral, Opinion/attitude
measurement toolObservational rating
item formatLikert Scale/selected response
primary language(s)eng
construct nameLegislative preference

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, issueirt_votes_shin_2024.R. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.

ColumnWhat it isSource column
IRW standard columns
idIRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).made in the build script
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
waveIRW standard Longitudinal wave indicator.H52

What item codes and covariate values stand for is in the codebook released with the source data.

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("issueirt_votes_shin_2024")
# Python
pip install irw

import irw
df = irw.fetch("issueirt_votes_shin_2024")

How to cite

@phdthesis{Shin2024,
  author    = {Shin, Sooahn},
  title     = {Measuring Issue Specific Ideal Points from Roll Call Votes},
  year      = {2024},
  school    = {Harvard University},
  type      = {Doctoral dissertation},
  note      = {Ph. D. Candidate, Department of Government and Institute for Quantitative Social Science},
  url       = {https://github.com/sooahnshin/issueirt/blob/main/data/us1890s_votes.rda}
}

Version and provenance

IRW versionv557
Redivis datasetitem_response_warehouse_2 v33.0
Redivis dataset DOI10.57761/6m04-m698
Manifest pin for this IRW versionv33.0
Metadata sourceirw_meta v40.0