901 responses from 451 respondents to 2 items.
| Description | Magic Curiosity Arousing Tricks (MagicCATs): A novel stimulus collection to induce epistemic emotions |
|---|---|
| Reference | Haffey, A., Murayama, K., Ozono, H., Lau, J. K. L., & Meliss, S. (2025, September 12). Magic CATs (Curiosity Arousing Tricks) videos. Retrieved from osf.io/ad6uc |
| DOI | 10.3758/s13428-020-01431-2 |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/ad6uc/overview |
| Responses | 901 |
|---|---|
| Respondents | 451 |
| Items | 2 |
| Response categories | 10 |
| Responses per respondent | 1.998 |
| Responses per item | 450.500 |
| Density | 0.999 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | General/non-specific |
| construct type | Affective/mental health |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Epistemic Emotions |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | MagicCATs post-experiment ratings (interest in magic tricks; performing magic tricks) |
|---|---|
| Mean words per item | 5.769 |
| Mean characters per item | 34.692 |
| Mean characters per response | 5.154 |
| Flesch-Kincaid grade level | 6.641 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, magiccats_ozono_2020.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). | Participant ID |
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_gender | Covariate | Gender |
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("magiccats_ozono_2020")
# Python
pip install irw
import irw
df = irw.fetch("magiccats_ozono_2020")
@article{Ozono_2020, title={Magic Curiosity Arousing Tricks (MagicCATs): A novel stimulus collection to induce epistemic emotions}, volume={53}, ISSN={1554-3528}, url={http://dx.doi.org/10.3758/s13428-020-01431-2}, DOI={10.3758/s13428-020-01431-2}, number={1}, journal={Behavior Research Methods}, publisher={Springer Science and Business Media LLC}, author={Ozono, Hiroki and Komiya, Asuka and Kuratomi, Kei and Hatano, Aya and Fastrich, Greta and Raw, Jasmine April Louise and Haffey, Anthony and Meliss, Stefanie and Lau, Johnny King L. and Murayama, Kou}, year={2020}, month=jul, pages={188–215} }
| IRW version | v557 |
|---|---|
| Redivis dataset | item_response_warehouse_2 v33.0 |
| Redivis dataset DOI | 10.57761/6m04-m698 |
| Manifest pin for this IRW version | v33.0 |
| Metadata source | irw_meta v40.0 |