23,574 responses from 1,324 respondents to 18 items.
One of 5 tables from the same source; this one is the peer_relationship table. The others are listed under Related tables.
| Description | 18-item Peer-Relationship Scale (0-6 scale), from a survey of N=1324 Chinese adolescents on AI in education and social adaptability. |
|---|---|
| Reference | Lai T, Xie C, Ruan M, Wang Z, Lu H, Fu S (2023) Influence of artificial intelligence in education on adolescents' social adaptability: The mediatory role of social support. PLoS ONE. |
| DOI | 10.1371/journal.pone.0283170 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0283170.s001 |
| Responses | 23,574 |
|---|---|
| Respondents | 1,324 |
| Items | 18 |
| Response categories | 6 |
| Responses per respondent | 17.805 |
| Responses per item | 1309.667 |
| Density | 0.989 |
| Longitudinal | FALSE |
| age range | Child (<18y) |
|---|---|
| child age (for child-focused studies) | Child (6-12y), Adolescent (12-18y) |
| sample | Educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, lai_2023_social_adaptability.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.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
id | IRW 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 |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_age | Covariate | AGE |
cov_gender | Covariate | GENDER |
cov_grade | Covariate | GRADE |
cov_school | Covariate | SCHOOLNAME |
What item codes and covariate values stand for is in the codebook released with the source data.
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("lai_2023_peer_relationship")
# Python
pip install irw
import irw
df = irw.fetch("lai_2023_peer_relationship")
@article{Lai_2023, title={Influence of artificial intelligence in education on adolescents’ social adaptability: The mediatory role of social support}, volume={18}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0283170}, DOI={10.1371/journal.pone.0283170}, number={3}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Lai, Tinghong and Xie, Chuyin and Ruan, Minhua and Wang, Zheng and Lu, Hong and Fu, Shimin}, editor={Uddin, Md Zia}, year={2023}, month=Mar, pages={e0283170} }
| IRW version | v557 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v13.0 |
| Redivis dataset DOI | 10.57761/w8zx-td92 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v40.0 |