8,652 responses from 309 respondents to 28 items.
| Description | Chinese version of the General Benefit Finding Scale (28 items), college students |
|---|---|
| Reference | Hui Z, Wang X, Teng Z, Zou W, Wang J, Ji P, Wang M (2024). The Chinese version of the general benefit finding scale (GBFS): Psychometric properties in a sample of college students. PLOS ONE, 19(5), e0300064. https://doi.org/10.1371/journal.pone.0300064 |
| DOI | 10.1371/journal.pone.0300064 |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.1371/journal.pone.0300064.s002 |
| Responses | 8,652 |
|---|---|
| Respondents | 309 |
| Items | 28 |
| Response categories | 5 |
| Responses per respondent | 28 |
| Responses per item | 309 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Educational |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | General Benefit Finding Scale (GBFS), Chinese version (益处发现量表) |
|---|---|
| Mean words per item | 7.964 |
| Mean characters per item | 41.286 |
| Mean characters per response | 3.600 |
| Flesch-Kincaid grade level | 4.449 |
iditemresp
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("hui_2024_gbfs")
# Python
pip install irw
import irw
df = irw.fetch("hui_2024_gbfs")
| IRW version | v393 |
|---|---|
| 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 |