cox_2024_feedback_perceptions

265 responses from 29 respondents to 5 items.

About this table

Description5-item feedback-perceptions survey, first-year medical residents, pre/post intervention (wave=1/2), 1-5 Likert, N=29
ReferenceCox, R., & Arthur, J. (2024). Feedback perceptions of first year medical residents: An intervention-based survey study. PLOS ONE.
DOI10.1371/journal.pone.0300205
LicenceCC BY 4.0
Source datahttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0300205

Size and shape

Responses265
Respondents29
Items5
Response categories3
Responses per respondent9.138
Responses per item53
Density1.828
LongitudinalTRUE

Classification

sampleWorkplace
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.

InstrumentFeedback perceptions questionnaire
Mean words per item11.800
Mean characters per item75.800
Mean characters per response9
Flesch-Kincaid grade level10.412

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, cox_2024_feedback_perceptions.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).Participant Code
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
waveIRW standard Longitudinal wave indicator.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("cox_2024_feedback_perceptions")
# Python
pip install irw

import irw
df = irw.fetch("cox_2024_feedback_perceptions")

How to cite

@article{Cox_2024, title={Feedback perceptions of first year medical residents: An intervention-based survey study}, volume={19}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0300205}, DOI={10.1371/journal.pone.0300205}, number={4}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Cox, Rachel and Arthur, John and Burtson, Kathryn}, editor={Mitra, Souparno}, year={2024}, month=Apr, pages={e0300205} }

Version and provenance

IRW versionv557
Redivis datasetitem_response_warehouse_3 v13.0
Redivis dataset DOI10.57761/gpg7-sg87
Manifest pin for this IRW versionv13.0
Metadata sourceirw_meta v40.0