gilbert_meta_104

16,803 responses from 1,914 respondents to 9 items.

About this table

Descriptionbackground science knowledge
ReferenceRelyea, J. E., Gilbert, J. B., Burkhauser, M., Scherer, E., Mosher, D. M., Wei, Z., ... & Kim, J. S. (2025). Asset‐Based Implementation of Structured Adaptations in an Online Third‐Grade Content Literacy Intervention. Reading Research Quarterly, 60(4), e70048.
DOI10.1002/rrq.70048
LicenceCC BY-NC 4.0
Source datahttps://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/JDUIKT

Size and shape

Responses16,803
Respondents1,914
Items9
Response categories2
Responses per respondent8.779
Responses per item1,867
Density0.975
LongitudinalFALSE

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 namebackground science knowledge

Item text

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

Instrumentbackground science knowledge
Mean words per item11.556
Mean characters per item59.111
Mean characters per response12.833
Flesch-Kincaid grade level2.550

Columns

block_idcluster_idcov_blackcov_homelang_englishcov_iepcov_lepcov_malecov_ses_highcov_ses_lowcov_ses_medi_rtiditemitem_numln_rtresprts_rtstd_baselinetreat

Get the data

Licence: CC BY-NC 4.0 — non-commercial use only.

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_104")
# Python
pip install irw

import irw
df = irw.fetch("gilbert_meta_104")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse_2 v19.0
Redivis dataset DOI10.57761/qtx5-4m80
Manifest pin for this IRW versionv19.0
Metadata sourceirw_meta v23.0