konerding_2019_patientsatisfaction

13,188 responses from 1,884 respondents to 7 items.

About this table

Description7-item short patient satisfaction questionnaire based on SERVQUAL (6 perception items + 1 overall satisfaction item, 1-7 scale), from a survey of N=1884 diabetes and stroke patients across six European countries.
ReferenceKonerding U, Bowen T, Elkhuizen S, Faubel R, Forte P, Karampli E, et al. (2019) Development of a universal short patient satisfaction questionnaire on the basis of SERVQUAL: Psychometric analyses with data of diabetes and stroke patients from six different European countries. PLoS ONE.
DOI10.1371/journal.pone.0197924
LicenceCC BY 4.0
Source datahttps://doi.org/10.1371/journal.pone.0197924.s001

Size and shape

Responses13,188
Respondents1,884
Items7
Response categories7
Responses per respondent7
Responses per item1,884
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleClinical
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response

Item text

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

InstrumentUniversal short patient satisfaction questionnaire based on SERVQUAL
Mean words per item9.714
Mean characters per item71.429
Mean characters per response6.061
Flesch-Kincaid grade level12.913

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, konerding_2019_patientsatisfaction.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 Age
cov_conditionCovariate Med_Cond
cov_countryCovariate Country
cov_educationCovariate Education
cov_genderCovariate Gender
cov_masters_main_languageCovariate MasterMainLang
cov_mother_languageCovariate Motherlang

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("konerding_2019_patientsatisfaction")
# Python
pip install irw

import irw
df = irw.fetch("konerding_2019_patientsatisfaction")

How to cite

@article{Konerding_2019, title={Development of a universal short patient satisfaction questionnaire on the basis of SERVQUAL: Psychometric analyses with data of diabetes and stroke patients from six different European countries}, volume={14}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0197924}, DOI={10.1371/journal.pone.0197924}, number={10}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Konerding, Uwe and Bowen, Tom and Elkhuizen, Sylvia G. and Faubel, Raquel and Forte, Paul and Karampli, Eleftheria and Malmström, Tomi and Pavi, Elpida and Torkki, Paulus}, editor={Lin, Chung-Ying}, year={2019}, month=Oct, pages={e0197924} }

Version and provenance

IRW versionv549
Redivis datasetitem_response_warehouse_4 v13.0
Redivis dataset DOI10.57761/w8zx-td92
Manifest pin for this IRW versionv13.0
Metadata sourceirw_meta v39.0