gilbert_meta_80

50,940 responses from 1,189 respondents to 36 items.

About this table

DescriptionWoodcock–Johnson III Tests of Achievement Picture Vocabulary subtest
ReferenceCabell, S. Q., Kim, J. S., White, T. G., Gale, C. J., Edwards, A. A., Hwang, H., ... & Raines, R. M. (2024). Impact of a content-rich literacy curriculum on kindergarteners’ vocabulary, listening comprehension, and content knowledge. Journal of Educational Psychology.
DOI10.1037/edu0000916
LicenceODC-By
Source datahttps://ldbase.org/datasets/de4bf144-39ed-430c-862d-201009d3d33e

Size and shape

Responses50,940
Respondents1,189
Items36
Response categories2
Responses per respondent42.843
Responses per item1,415
Density1.190
LongitudinalTRUE

Classification

age rangeChild (<18y)
child age (for child-focused studies)Child (6-12y)
sampleEducational
construct typeCognitive/educational
measurement toolTest
item formatLikert Scale/selected response
primary language(s)eng
construct nameWoodcock–Johnson III Tests of Achievement Picture Vocabulary subtest

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

InstrumentWoodcock–Johnson III Tests of Achievement Picture Vocabulary subtest
Mean words per item1.159
Mean characters per item7.886
Flesch-Kincaid grade level15.312

Columns

cluster_idcov_gradecov_malecov_teacheriditemresptreatwave

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python:

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("gilbert_meta_80")
# Python
pip install irw

import irw
df = irw.fetch("gilbert_meta_80")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse v53.0
Redivis dataset DOI10.57761/4g08-xt41
Manifest pin for this IRW versionv53.0
Metadata sourceirw_meta v23.0