bate_2019_srq

8,300 responses from 415 respondents to 20 items.

About this table

DescriptionSuper-Recognizer Questionnaire (SRQ), 20 self-report items on a 1-5 scale; 415 adults pooled from a top-end civilian sample (n=264) and a police sample (n=151).
ReferenceBate S, Dudfield G. 2019. Subjective assessment for super recognition: an evaluation of self-report methods in civilian and police participants. PeerJ 7:e6330.
DOI10.7717/peerj.6330
LicenceCC BY 4.0
Source datahttps://peerj.com/articles/6330/#supplementary-material

Size and shape

Responses8,300
Respondents415
Items20
Response categories5
Responses per respondent20
Responses per item415
Density1
LongitudinalFALSE

Classification

sampleTargeted/specific
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

InstrumentSuper Recognizer Questionnaire (SRQ)
Mean words per item12.100
Mean characters per item67.800
Mean characters per response7.400
Flesch-Kincaid grade level7.255

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, bate_2019_srq.py. 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
Respondent covariates
cov_ageCovariate made in the build script
cov_genderCovariate made in the build script
cov_groupCovariate made in the build script
cov_studyCovariate 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("bate_2019_srq")
# Python
pip install irw

import irw
df = irw.fetch("bate_2019_srq")

How to cite

@article{Bate_2019, title={Subjective assessment for super recognition: an evaluation of self-report methods in civilian and police participants}, volume={7}, ISSN={2167-8359}, url={http://dx.doi.org/10.7717/peerj.6330}, DOI={10.7717/peerj.6330}, journal={PeerJ}, publisher={PeerJ}, author={Bate, Sarah and Dudfield, Gavin}, year={2019}, month=Jan, pages={e6330} }

Version and provenance

IRW versionv566
Redivis datasetitem_response_warehouse_6 v5.11
Redivis dataset DOI10.57761/qahq-j855
Manifest pin for this IRW versionv5.11
Metadata sourceirw_meta v43.0