4,553 responses from 702 respondents to 10 items.
| Description | Duke Social Support and Stress Scale supportiveness items (10 items, 1-3) from 702 medical students at the Chinese University of Hong Kong, 2017; the "there is no such person" category is coded missing. |
|---|---|
| Reference | Lee KP, Yeung N, Wong C, Yip B, Luk LHF, Wong S (2020). Prevalence of medical students' burnout and its associated demographics and lifestyle factors in Hong Kong. PLOS ONE 15(7): e0235154. https://doi.org/10.1371/journal.pone.0235154 |
| DOI | 10.1371/journal.pone.0235154 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235154 |
| Responses | 4,553 |
|---|---|
| Respondents | 702 |
| Items | 10 |
| Response categories | 3 |
| Responses per respondent | 6.486 |
| Responses per item | 455.300 |
| Density | 0.649 |
| Longitudinal | FALSE |
| age range | Mixed |
|---|---|
| child age (for child-focused studies) | Adolescent (12-18y) |
| sample | Educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
cov_agecov_clinical_stagecov_first_degreecov_gendercov_marital_statuscov_place_of_livingcov_scholarshipcov_smokecov_study_yeariditemresp
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("lee_2020_social_support")
# Python
pip install irw
import irw
df = irw.fetch("lee_2020_social_support")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v7.0 |
| Redivis dataset DOI | 10.57761/cpj5-vm97 |
| Manifest pin for this IRW version | v7.0 |
| Metadata source | irw_meta v23.0 |