56,592 responses from 786 respondents to 72 items.
| Description | Data from a spelling test |
|---|---|
| Reference | Eskenazi, M. A., Askew, R. L., & Folk, J. R. (2023). Precision in the measurement of lexical expertise: the selection of optimal items for a spelling assessment. Behavior Research Methods, 55(2), 623-632. |
| DOI | 10.3758/s13428-022-01834-3 |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/7tuwr/?view_only=c62d60752bbd48fa8dd7cc3ee092ea51 |
| Responses | 56,592 |
|---|---|
| Respondents | 786 |
| Items | 72 |
| Response categories | 2 |
| Responses per respondent | 72 |
| Responses per item | 786 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Educational, Internet-based |
| construct type | Cognitive/educational |
| measurement tool | Test |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | spelling assessment |
groupiditemresp
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("spelling_assessment_study2")
# Python
pip install irw
import irw
df = irw.fetch("spelling_assessment_study2")
| 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 |