5,850 responses from 117 respondents to 50 items.
| Description | Item-level binary (correct/incorrect) responses on a 50-item women's health nursing examination in Korea; 117 examinees including 111 nursing students and 6 generative AI platforms |
|---|---|
| Reference | Lee, T.K., Jeong, G.H. (2025). Comparing generative artificial intelligence platforms and nursing student performance on a women's health nursing examination in Korea: a Rasch model approach. Journal of Educational Evaluation for Health Professions, 22, 23. https://doi.org/10.3352/jeehp.2025.22.23 |
| DOI | 10.3352/jeehp.2025.22.23 |
| Licence | CC0 1.0 |
| Source data | https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PWV6H2 |
| Responses | 5,850 |
|---|---|
| Respondents | 117 |
| Items | 50 |
| Response categories | 2 |
| Responses per respondent | 50 |
| Responses per item | 117 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Educational |
|---|---|
| measurement tool | Test |
| item format | Likert Scale/selected response |
| primary language(s) | kor |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | 2023 Women's Health Nursing final examination, 4th-year first semester, Korean baccalaureate nursing programme |
|---|---|
| Mean words per item | 39.320 |
| Mean characters per item | 276.720 |
| Mean characters per response | 8 |
| Flesch-Kincaid grade level | 11.301 |
iditemresp
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("lee_2025_nursing_exam")
# Python
pip install irw
import irw
df = irw.fetch("lee_2025_nursing_exam")
| 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 |