9,971 responses from 1,108 respondents to 9 items.
| Description | IGDS9-SF (Internet Gaming Disorder Scale-Short Form), 1-5 Likert; N=1108, 9 items, Chinese primary school students |
|---|---|
| Reference | Ma, J., An, S., Chen, Q., & Liu, S. (2026). Assessing Online-Related Addiction in Chinese Primary School Students: An Item Response Theory Analysis of Three Scales. Research Square (preprint). |
| DOI | 10.21203/rs.3.rs-9429022/v1 |
| Licence | CC BY 4.0 |
| Source data | https://figshare.com/articles/dataset/Assessing_Online-Related_Addiction_in_Chinese_Primary_School_Students_An_Item_Response_Theory_Analysis_of_Three_Scales/27211839 |
| Responses | 9,971 |
|---|---|
| Respondents | 1,108 |
| Items | 9 |
| Response categories | 5 |
| Responses per respondent | 8.999 |
| Responses per item | 1107.889 |
| Density | 1.000 |
| Longitudinal | TRUE |
| sample | Educational |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
cov_assess_datecov_gradecov_racecov_sexcov_sickiditemresp
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("ma2026_igds")
# Python
pip install irw
import irw
df = irw.fetch("ma2026_igds")
Download as CSV (no account needed), browse it on Redivis, or take the Croissant description of this table for use with Hugging Face, Kaggle or OpenML.
| IRW version | v385 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v7.0 |
| Redivis dataset DOI | 10.57761/pqqn-pm43 |
| Manifest pin for this IRW version | v7.0 |
| Metadata source | irw_meta v23.0 |