1,035 responses from 345 respondents to 3 items.
One of 6 tables from the same source; this one is the sni table. The others are listed under Related tables.
| Description | 3-item Social Networking Intensity scale (7-point Likert, 1-7), from a COVID-19 survey of N=345 Korean and Vietnamese university students. |
|---|---|
| Reference | Jo H, Baek E (2023) Impacts of social isolation and risk perception on social networking intensity among university students during covid-19. PLoS ONE. |
| DOI | 10.1371/journal.pone.0283997 |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.1371/journal.pone.0283997.s003 |
| Responses | 1,035 |
|---|---|
| Respondents | 345 |
| Items | 3 |
| Response categories | 7 |
| Responses per respondent | 3 |
| Responses per item | 345 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
These are classifications the IRW records, not the study’s own words. Some were annotated by hand and some written by an automated tagger whose output is published only where it clears a measured accuracy bar; which values came from which is not recorded per table. Coverage and accuracy per column, and what is held back, are on Tag quality.
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Social Networking Intensity (3 items, adapted from Eid & Al-Jabri 2016) |
|---|---|
| Mean words per item | 12 |
| Mean characters per item | 86.667 |
| Mean characters per response | 2 |
| Flesch-Kincaid grade level | 11.265 |
This codebook is reconstructed by the IRW from the IRW data standard. 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). | not traced |
item | IRW standard Item identifier. | not traced |
resp | IRW standard Response value. | not traced |
| Respondent covariates | ||
cov_age | Covariate | not traced |
cov_gender_male | Covariate | not traced |
cov_nationality | Covariate | not traced |
What item codes and covariate values stand for is in the source’s documentation, S1_Appendix.docx.
Respondent uniqueness. 2% of this table’s respondents are alone in their combination of the 2 covariates above — no one else in the table shares it. That is a necessary condition for re-identification and not re-identification itself, and on a sample this size it is largely arithmetic. The IRW republishes the demographics its sources published and never at finer granularity: see the data standard.
Or load it directly in R (no account needed with irw 1.4.0 or later) or Python (needs a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("irw")
library(irw)
df <- irw_fetch("jo_2023_sni")
# Python
pip install irw
import irw
df = irw.fetch("jo_2023_sni")
Or in Stata, which reads the same no-account CSV as the button above:
* Stata
net install irw, from("https://raw.githubusercontent.com/itemresponsewarehouse/Stata-pkg/main/")
irw_fetch jo_2023_sni, clear
@article{Jo_2023, title={Impacts of social isolation and risk perception on social networking intensity among university students during covid-19}, volume={18}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0283997}, DOI={10.1371/journal.pone.0283997}, number={4}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Jo, Hyeon and Baek, Eun-Mi}, editor={Al-Adwan, Ahmad Samed}, year={2023}, month=Apr, pages={e0283997} }
| IRW version | v674 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v16.0 |
| Redivis dataset DOI | 10.57761/3rv0-z494 |
| Manifest pin for this IRW version | v16.0 |
| Metadata source | irw_meta v59.0 |