29,400 responses from 1,225 respondents to 12 items.
| Description | Assessment from a literacy intervention |
|---|---|
| Reference | Gilbert, J. B., Kim, J. S., & Miratrix, L. W. (2024). Leveraging item parameter drift to assess transfer effects in vocabulary learning. Applied Measurement in Education, 37(3), 240-257. Gilbert, Josh; Kim, James; Miratrix, Luke, 2024, "Replication Data for: Leveraging Item Parameter Drift to Assess Transfer Effects in Vocabulary Learning", https://doi.org/10.7910/DVN/ZF1LKZ, Harvard Dataverse, V2, UNF:6:A83pi+tE9hP611HoBh7Pug== [fileUNF] |
| Licence | CC BY-NC-SA 4.0 |
| Source data | https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZF1LKZ |
| Responses | 29,400 |
|---|---|
| Respondents | 1,225 |
| Items | 12 |
| Response categories | 2 |
| Responses per respondent | 24 |
| Responses per item | 2,450 |
| Density | 2 |
| Longitudinal | TRUE |
| construct name | Leveraging Item Parameter Drift to Assess Transfer Effects in Vocabulary Learning |
|---|
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Assessment from a literacy intervention |
|---|---|
| Mean words per item | 10 |
| Mean characters per item | 52.667 |
| Mean characters per response | 4.944 |
| Flesch-Kincaid grade level | 2.175 |
cluster_ididitemrespstd_baselinetreatwave
Licence: CC BY-NC-SA 4.0 — non-commercial use only; adaptations must be shared under the same licence.
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("gilbert_meta_74")
# Python
pip install irw
import irw
df = irw.fetch("gilbert_meta_74")
| 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 |