53,245 responses from 48 respondents to 6,108 items.
| Description | Supreme Court voting record |
|---|---|
| Reference | Martin, A. D., & Quinn, K. M. (2002). Dynamic ideal point estimation via Markov chain Monte Carlo for the US Supreme Court, 1953–1999. Political analysis, 10(2), 134-153. |
| DOI | 10.1093/pan/10.2.134 |
| Licence | CC BY-NC 3.0 |
| Source data | https://mqscores.lsa.umich.edu/replication.php |
| Responses | 53,245 |
|---|---|
| Respondents | 48 |
| Items | 6,108 |
| Response categories | 2 |
| Responses per respondent | 1109.271 |
| Responses per item | 8.717 |
| Density | 0.182 |
| Longitudinal | TRUE |
| age range | Adult (18+) |
|---|---|
| sample | Targeted/specific |
| construct type | Opinion/attitude |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Supreme Court voting record |
dateiditemrespterm
Licence: CC BY-NC 3.0 — non-commercial use only.
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("mq_supremecourt")
# Python
pip install irw
import irw
df = irw.fetch("mq_supremecourt")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse v53.0 |
| Redivis dataset DOI | 10.57761/4g08-xt41 |
| Manifest pin for this IRW version | v53.0 |
| Metadata source | irw_meta v23.0 |