21,960 responses from 92 respondents to 10 items.
| Description | Human ratings of a divergent thinking measure |
|---|---|
| Reference | Dumas, D., Organisciak, P., & Doherty, M. (2021). Measuring divergent thinking originality with human raters and text-mining models: A psychometric comparison of methods. Psychology of Aesthetics, Creativity, and the Arts, 15(4), 645. |
| DOI | 10.1037/aca0000319 |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/qcmex/ |
| Responses | 21,960 |
|---|---|
| Respondents | 92 |
| Items | 10 |
| Response categories | 5 |
| Responses per respondent | 238.696 |
| Responses per item | 2,196 |
| Density | 23.870 |
| Longitudinal | FALSE |
| age range | Mixed |
|---|---|
| sample | Educational, Targeted/specific |
| construct type | Cognitive/educational |
| measurement tool | Survey/questionnaire |
| item format | Constructed Response |
| primary language(s) | eng |
| construct name | Measuring divergent thinking originality with human raters and text-mining models |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Mean words per item | 1 |
|---|---|
| Mean characters per item | 4.700 |
| Mean characters per response | 7.400 |
| Flesch-Kincaid grade level | 20.430 |
iditemraterrespverbatim
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("dumas_Organisciak_2022")
# Python
pip install irw
import irw
df = irw.fetch("dumas_Organisciak_2022")
| 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 |