6,360 responses from 636 respondents to 10 items.
One of 4 tables from the same source; this one measures Dealing with Boredom Scale. The others are listed under Related tables.
| Description | Dealing with boredom scale |
|---|---|
| Reference | Bieleke, M., Ripper, L., Schüler, J., & Wolff, W. (2022). Boredom is the root of all evil—or is it? A psychometric network approach to individual differences in behavioural responses to boredom. Royal Society open science, 9(9), 211998. |
| DOI | 10.1098/rsos.211998 |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/nbw95/ |
| Responses | 6,360 |
|---|---|
| Respondents | 636 |
| Items | 10 |
| Response categories | 7 |
| Responses per respondent | 10 |
| Responses per item | 636 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | General/non-specific |
| construct type | Behavioral |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Dealing with Boredom Scale |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Dealing with boredom scale |
|---|---|
| Mean words per item | 8.200 |
| Mean characters per item | 39 |
| Mean characters per response | 14.429 |
| Flesch-Kincaid grade level | 3.006 |
This codebook is reconstructed by the IRW from the IRW data standard. 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). | not traced |
item | IRW standard Item identifier. | not traced |
resp | IRW standard Response value. | not traced |
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:
# R (no account needed)
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("ada_boredom_bieleke2022")
# Python (needs a free Redivis account)
pip install irw
import irw
df = irw.fetch("ada_boredom_bieleke2022")
@article{Bieleke_2022, title={Boredom is the root of all evil—or is it? A psychometric network approach to individual differences in behavioural responses to boredom}, volume={9}, ISSN={2054-5703}, url={http://dx.doi.org/10.1098/rsos.211998}, DOI={10.1098/rsos.211998}, number={9}, journal={Royal Society Open Science}, publisher={The Royal Society}, author={Bieleke, Maik and Ripper, Leonie and Schüler, Julia and Wolff, Wanja}, year={2022}, month=sep }
| IRW version | v540 |
|---|---|
| Redivis dataset | item_response_warehouse v70.0 |
| Redivis dataset DOI | 10.57761/4qxs-pq87 |
| Manifest pin for this IRW version | v70.0 |
| Metadata source | irw_meta v37.0 |