About this table

Description	Metacognitions Questionnaire-30 (Turkish), 30 items, 1-4; N = 210 (105 chronic-pain patients, 105 controls); cov_group plus demographics and VAS pain.
Reference	Çelikbaş, Z. (2022). Pain Metacognition Questionnaire [Dataset]. figshare. https://doi.org/10.6084/m9.figshare.19750066
Licence	CC BY 4.0
Source data	https://figshare.com/articles/dataset/19750066

Size and shape

Responses	6,300
Respondents	210
Items	30
Response categories	4
Responses per respondent	30
Responses per item	210
Density	1
Longitudinal	FALSE

Classification

age range	Adult (18+)
sample	Clinical, General/non-specific
construct type	Affective/mental health
measurement tool	Survey/questionnaire
item format	Likert Scale/selected response
primary language(s)	tur
construct name	Metacognitive beliefs (MCQ-30)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, celikbas_2022_pain_metacognition.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_comorbidity`	Covariate	`ek_hastalik`
`cov_education_years`	Covariate	`ögrenimsüresi`
`cov_employment`	Covariate	`calisma_durumu`
`cov_gender`	Covariate	`cinsiyet`
`cov_group`	Covariate	`gruplar`
`cov_marital`	Covariate	`medenidurum`
`cov_ses`	Covariate	`sosyoekonomik_düzey`
`cov_substance_use`	Covariate	`Madde_kullanimi`
`cov_vas_pain`	Covariate	`Görsel_agri_skalasi`

What item codes and covariate 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 (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("celikbas_2022_mcq30")

# Python
pip install irw

import irw
df = irw.fetch("celikbas_2022_mcq30")

How to cite

@misc{https://doi.org/10.6084/m9.figshare.19750066,
  doi = {10.6084/M9.FIGSHARE.19750066},
  url = {https://figshare.com/articles/dataset/Pain_Metacognition_Questionnaire/19750066},
  author = {çelikbaş, zekiye},
  keywords = {179999 Psychology and Cognitive Sciences not elsewhere classified, FOS: Psychology},
  title = {Pain Metacognition Questionnaire},
  publisher = {figshare},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

Version and provenance

IRW version	v584
Redivis dataset	item_response_warehouse_5 v8.5
Redivis dataset DOI	10.57761/41xy-5k56
Manifest pin for this IRW version	v8.5
Metadata source	irw_meta v44.0

celikbas_2022_mcq30