About this table

Description	22-item mental health literacy knowledge scale scored right/wrong; Turkish adults. 240 respondents x 22 items = 5276 responses.
Reference	Eyrenci, E. M., & Ozturk Turgut, E. (2025). mental health literacy [Data set]. Mendeley Data. https://doi.org/10.17632/dmv73kyjdp
Licence	CC BY 4.0
Source data	https://data.mendeley.com/datasets/dmv73kyjdp

Size and shape

Responses	5,276
Respondents	240
Items	22
Response categories	2
Responses per respondent	21.983
Responses per item	239.818
Density	0.999
Longitudinal	FALSE

Classification

age range	Adult (18+)
sample	Clinical
measurement tool	Test
item format	Likert Scale/selected response
primary language(s)	tur

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, eyrenci_2025_mental_health_literacy.py. 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).	made in the build script
`item`	IRW standard Item identifier.	made in the build script
`resp`	IRW standard Response value.	made in the build script
Respondent covariates
`cov_age`	Covariate	`yas`
`cov_education`	Covariate `1` = okuryazar/ilkokul · `2` = ortaokul lise · `3` = önlisans lisans · `4` = lisansüstü	`egitim`
`cov_employment`	Covariate `1` = evet · `2` = hayır	`calısma`
`cov_has_children`	Covariate `1` = evet · `2` = hayır	`cocuk`
`cov_health_worker`	Covariate	`saglıkcalısan`
`cov_marital_status`	Covariate `1` = bekar · `2` = evli	`medeni`
`cov_n_children`	Covariate	`cocuksayı`
`cov_physical_illness`	Covariate `1` = evet · `2` = hayır	`baskafizhas`
`cov_psychiatric_history`	Covariate `1` = evet · `2` = hayır	`baskapsik`
`cov_sex`	Covariate `1` = kadın · `2` = erkek	`cisn`

Covariate value labels are the source’s own, as the IRW recorded them. What item codes and any unlabelled values stand for is in the codebook released with the source data.

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python:

# R (no account needed)
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("eyrenci_2025_mental_health_literacy")

# Python (needs a free Redivis account)
pip install irw

import irw
df = irw.fetch("eyrenci_2025_mental_health_literacy")

How to cite

@misc{https://doi.org/10.17632/dmv73kyjdp,
  doi = {10.17632/DMV73KYJDP},
  url = {https://data.mendeley.com/datasets/dmv73kyjdp},
  author = {Eyrenci, Ege Miray and Öztürk Turgut, Emel},
  keywords = {Psychiatry, Nursing, FOS: Health sciences, Cancer, Chemotherapy, Liaison Nursing, Community Mental Health, Health Literacy},
  title = {mental health literacy},
  publisher = {Mendeley Data},
  year = {2025}
}

Version and provenance

IRW version	v540
Redivis dataset	item_response_warehouse_4 v13.0
Redivis dataset DOI	10.57761/w8zx-td92
Manifest pin for this IRW version	v13.0
Metadata source	irw_meta v37.0