4,600 responses from 230 respondents to 20 items.
| Description | Attentional Control Scale |
|---|---|
| Reference | Schubert, A.-L., Frischkorn, G. T., Sadus, K., Welhaf, M., Kane, M. J., & Rummel, J. (2024, June 13). Online Supplement: The brief mind wandering three-factor scale. Retrieved from osf.io/mxn3v |
| DOI | 10.3758/s13428-024-02500-6 |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/mxn3v/ |
| Responses | 4,600 |
|---|---|
| Respondents | 230 |
| Items | 20 |
| Response categories | 4 |
| Responses per respondent | 20 |
| Responses per item | 230 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Educational, General/non-specific |
| construct type | Cognitive/educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | ger, eng |
| construct name | Attentional Control Scale |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Attentional Control Scale |
|---|---|
| Mean words per item | 14.900 |
| Mean characters per item | 79.700 |
| Mean characters per response | 8 |
| Flesch-Kincaid grade level | 7.644 |
iditemresp
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("OS_TBMWTFS_Schubert_2023_ACS")
# Python
pip install irw
import irw
df = irw.fetch("OS_TBMWTFS_Schubert_2023_ACS")
| 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 |