figure_skating

23,077 responses from 1,676 respondents to 663 items.

About this table

DescriptionJudges scores for different aspects of international figure skaters' performances (2016-17 and 2017-18 seasons)
ReferenceSinger-Vine, J. & Templon, J. (2021). ISU Figure Skating Score Sheets as Structured Data [data set]. BuzzFeed News. https://github.com/BuzzFeedNews/figure-skating-scores
LicenceCC BY 4.0
Source datahttps://github.com/BuzzFeedNews/figure-skating-scores

Size and shape

Responses23,077
Respondents1,676
Items663
Response categories1,103
Responses per respondent13.769
Responses per item34.807
Density0.021
LongitudinalFALSE

Classification

age rangeMixed
sampleGeneral/non-specific
construct typeBehavioral
measurement toolTest
item formatConstructed Response
primary language(s)eng
construct nameISU Figure Skating Judges' Aspect Scores Dataset

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, figure_skating.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.item_id
respIRW standard Response value.scores_of_panel
Other columns
nationNot documented Not documented by the IRW; see the source’s codebook.made in the build script
performance_idFrom source Not defined by the IRW standard; the source column’s codebook entry gives its meaning.performance
performance_typeNot documented Not documented by the IRW; see the source’s codebook.made in the build script
person_idNot documented Not documented by the IRW; see the source’s codebook.made in the build script

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("figure_skating")
# Python
pip install irw

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

How to cite

@misc{singer2021isu,
  author = {Singer-Vine, Jeremy and Templon, John},
  title = {ISU Figure Skating Score Sheets as Structured Data},
  year = {2021},
  publisher = {BuzzFeed News},
  howpublished = {\\url{https://github.com/BuzzFeedNews/figure-skating-scores}},
  note = {[data set]}
}

Version and provenance

IRW versionv584
Redivis datasetitem_response_warehouse v74.0
Redivis dataset DOI10.57761/a1gm-m985
Manifest pin for this IRW versionv73.0
Metadata sourceirw_meta v44.0