1,032 responses from 86 respondents to 6 items.
| Description | AI attitudes |
|---|---|
| Reference | Feray Ekin Çiçek, Müşerref Ülker, Menekşe Özer, Yavuz Selim Kıyak, ChatGPT versus expert feedback on clinical reasoning questions and their effect on learning: a randomized controlled trial, Postgraduate Medical Journal, 2024;, qgae170, https://doi.org/10.1093/postmj/qgae170 |
| DOI | 10.1093/postmj/qgae170 |
| Licence | CC BY 4.0 |
| Source data | https://zenodo.org/records/13769970 |
| Responses | 1,032 |
|---|---|
| Respondents | 86 |
| Items | 6 |
| Response categories | 7 |
| Responses per respondent | 12 |
| Responses per item | 172 |
| Density | 2 |
| Longitudinal | TRUE |
| age range | Adult (18+) |
|---|---|
| sample | Educational |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | tur |
| construct name | AI attitudes |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | AI attitudes |
|---|---|
| Mean words per item | 12.500 |
| Mean characters per item | 69 |
| Mean characters per response | 6.429 |
| Flesch-Kincaid grade level | 9.738 |
cov_malecov_repeat_yeariditemresptreatwave
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("gilbert_meta_96")
# Python
pip install irw
import irw
df = irw.fetch("gilbert_meta_96")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse v53.0 |
| Redivis dataset DOI | 10.57761/4g08-xt41 |
| Manifest pin for this IRW version | v53.0 |
| Metadata source | irw_meta v23.0 |