ALSECYPIAMH_WU_2022_SWLS

39,210 responses from 7,842 respondents to 5 items.

One of 10 tables from the same source; this one measures Satisfaction with Life Scale. The others are listed under Related tables.

About this table

DescriptionSatisfaction with Life Scale
ReferenceWu, Y., Yan, W., & Wu, Y. (2024, April 18). A large-scale evaluation of Chinese youth purpose and its associations with mental health. https://doi.org/10.17605/OSF.IO/JQZBX
LicenceCC BY 4.0
Source datahttps://osf.io/jqzbx/

Size and shape

Responses39,210
Respondents7,842
Items5
Response categories7
Responses per respondent5
Responses per item7,842
Density1
LongitudinalFALSE

Classification

age rangeChild (<18y)
child age (for child-focused studies)Adolescent (12-18y)
sampleEducational
construct typeAffective/mental health
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)chi, eng
construct nameSatisfaction with Life Scale

Item text

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

InstrumentSatisfaction with Life Scale
Mean words per item9.400
Mean characters per item45.400
Mean characters per response14.429
Flesch-Kincaid grade level3.140

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, ALSECYPIAMH_WU_2022.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).PID
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.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("ALSECYPIAMH_WU_2022_SWLS")
# Python
pip install irw

import irw
df = irw.fetch("ALSECYPIAMH_WU_2022_SWLS")

How to cite

@article{https://doi.org/10.17605/osf.io/jqzbx,
  doi = {10.17605/OSF.IO/JQZBX},
  url = {https://osf.io/jqzbx/},
  author = {Wu, Yun and Yan, Wei and Wu, Yiwen},
  title = {A large-scale evaluation of Chinese youth purpose and its associations with mental health},
  publisher = {Open Science Framework},
  year = {2022},
  copyright = {CC-By Attribution 4.0 International}
}

Version and provenance

IRW versionv569
Redivis datasetitem_response_warehouse v71.0
Redivis dataset DOI10.57761/f5n4-k797
Manifest pin for this IRW versionv70.0
Metadata sourceirw_meta v43.0