gilbert_meta_102

68,538 responses from 1,914 respondents to 36 items.

One of 3 tables from the same source; this one measures vocabulary test. The others are listed under Related tables.

About this table

Descriptionvocabulary test
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

Responses68,538
Respondents1,914
Items36
Response categories2
Responses per respondent35.809
Responses per item1903.833
Density0.995
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 namevocabulary test

Item text

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

Instrumentvocabulary test
Mean words per item2.028
Mean characters per item11.861
Mean characters per response4.889
Flesch-Kincaid grade level7.346

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, gilbert_meta_102through104.R. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.

ColumnWhat it isSource column
IRW standard columns
block_idIRW standard The level above cluster_id — in a randomised design, the block or stratum within which assignment was made (treat varies within a block, and clusters nest in blocks).school_name
cluster_idIRW standard The group a focal unit is nested in — classroom, teacher, school.teacher_name
idIRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).s_id
itemIRW standard Item identifier.not traced
respIRW standard Response value.not traced
rtIRW standard Response time.not traced
std_baselineIRW standard A standardised pre-treatment score, usually the trial's baseline measure.s_mapritread2021f_std
treatIRW standard Treatment group assignment in experimental studies.g3_treatment
Respondent covariates
cov_blackCovariate s_black
cov_homelang_englishCovariate s_homelang_english
cov_iepCovariate s_iep
cov_lepCovariate s_lep
cov_maleCovariate s_male
cov_ses_highCovariate s_ses_high
cov_ses_lowCovariate s_ses_low
cov_ses_medCovariate s_ses_med
Other columns
i_rtNot documented Not documented by the IRW; see the source’s codebook.not traced
item_numNot documented Not documented by the IRW; see the source’s codebook.not traced
ln_rtNot documented Not documented by the IRW; see the source’s codebook.not traced
s_rtNot documented Not documented by the IRW; see the source’s codebook.not traced

What item codes and covariate values stand for is in the source’s codebook, more_y4_tch_codebook.tab, more_y4_public_codebook.tab.

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 (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("gilbert_meta_102")
# Python
pip install irw

import irw
df = irw.fetch("gilbert_meta_102")

How to cite

@article{Relyea_2025, title={Asset‐Based Implementation of Structured Adaptations in an Online Third‐Grade Content Literacy Intervention}, volume={60}, ISSN={1936-2722}, url={http://dx.doi.org/10.1002/rrq.70048}, DOI={10.1002/rrq.70048}, number={4}, journal={Reading Research Quarterly}, publisher={Wiley}, author={Relyea, Jackie E. and Gilbert, Joshua B. and Burkhauser, Mary and Scherer, Ethan and Mosher, Douglas M. and Wei, Zhongyu and Tvedt, Johanna and Kim, James S.}, year={2025}, month=aug }

Version and provenance

IRW versionv549
Redivis datasetitem_response_warehouse_2 v33.0
Redivis dataset DOI10.57761/6m04-m698
Manifest pin for this IRW versionv33.0
Metadata sourceirw_meta v39.0