gan_2015_rrs

706 responses from 71 respondents to 10 items.

One of 3 tables from the same source; this one is the rrs table. The others are listed under Related tables.

About this table

DescriptionRuminative Response Scale, Chinese elderly nursing home residents
ReferenceGan, P., Xie, Y., Duan, W., Deng, Q., & Yu, X. (2015). Rumination and loneliness independently predict six-month later depression symptoms among Chinese elderly in nursing homes. PLoS ONE, 10(9), e0137176.
DOI10.1371/journal.pone.0137176
LicenceCC BY 4.0
Source datahttps://plos.figshare.com/articles/dataset/_Rumination_and_Loneliness_Independently_Predict_Six_Month_Later_Depression_Symptoms_among_Chinese_Elderly_in_Nursing_Homes_/1535084

Size and shape

Responses706
Respondents71
Items10
Response categories3
Responses per respondent9.944
Responses per item70.600
Density0.994
LongitudinalFALSE

Classification

age rangeElderly (minimum age >50)
sampleClinical
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)chi

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, gan_2015_nursing_home.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).num
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Respondent covariates
cov_ageCovariate Age
cov_educationCovariate Education
cov_genderCovariate Gender
cov_illness_countCovariate illness
cov_marriageCovariate Marriage

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("gan_2015_rrs")
# Python
pip install irw

import irw
df = irw.fetch("gan_2015_rrs")

How to cite

@article{Gan_2015, title={Rumination and Loneliness Independently Predict Six-Month Later Depression Symptoms among Chinese Elderly in Nursing Homes}, volume={10}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0137176}, DOI={10.1371/journal.pone.0137176}, number={9}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Gan, Pei and Xie, Yan and Duan, Wenjie and Deng, Qing and Yu, Xiuli}, editor={Laks, Jerson}, year={2015}, month=Sept, pages={e0137176} }

Version and provenance

IRW versionv549
Redivis datasetitem_response_warehouse_3 v13.0
Redivis dataset DOI10.57761/gpg7-sg87
Manifest pin for this IRW versionv13.0
Metadata sourceirw_meta v39.0