22,816 responses from 1,426 respondents to 16 items.
One of 7 tables from the same source; this one measures Ways of Coping (16-item short form). The others are listed under Related tables.
| Description | Ways of Coping, 16-item form, 0-3, N=1,426; Hungarian university students and staff, three cross-sectional pandemic-wave surveys; cov_sex (0 female, 1 male), cov_age, cov_survey_wave. |
|---|---|
| Reference | Matuz, A. (2024). Reframing prolonged negative mental health effects of COVID-19: Cognitive restructuring promotes posttraumatic growth [Data set]. Mendeley Data, V1. https://doi.org/10.17632/msxv59kwcx.1 |
| Licence | CC BY 4.0 |
| Source data | https://data.mendeley.com/datasets/msxv59kwcx/1 |
| Responses | 22,816 |
|---|---|
| Respondents | 1,426 |
| Items | 16 |
| Response categories | 4 |
| Responses per respondent | 16 |
| Responses per item | 1,426 |
| Density | 1 |
| Longitudinal | TRUE |
| age range | Adult (18+) |
|---|---|
| sample | Educational, Targeted/specific, Workplace |
| construct type | Behavioral |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | hun |
| construct name | Ways of Coping (16-item short form) |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, matuz_2024_covid_distress.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 | Age |
cov_sex | Covariate | Sex (0-female, 1-male) |
cov_survey_wave | Covariate | Wave |
What item codes and covariate values stand for is in the codebook released with the source data.
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("matuz_2024_woc")
# Python
pip install irw
import irw
df = irw.fetch("matuz_2024_woc")
@misc{https://doi.org/10.17632/msxv59kwcx.1,
doi = {10.17632/MSXV59KWCX.1},
url = {https://data.mendeley.com/datasets/msxv59kwcx/1},
author = {Matuz, Andras},
keywords = {Psychology, FOS: Psychology, Clinical Psychology},
title = {Reframing prolonged negative mental health effects of COVID-19: Cognitive restructuring promotes posttraumatic growth},
publisher = {Mendeley Data},
year = {2024}
}
| 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 |