gilbert_meta_7

49,259 responses from 1,386 respondents to 36 items.

About this table

DescriptionVocabulary test following a reading intervention
ReferenceKim, J. S., Gilbert, J. B., Relyea, J. E., Rich, P., Scherer, E., Burkhauser, M. A., & Tvedt, J. N. (2024). Time to transfer: Long-term effects of a sustained and spiraled content literacy intervention in the elementary grades.Developmental Psychology. Advance online publication. https://doi.org/10.1037/dev0001710 Kim, James S; Gilbert, Joshua B.; Relyea, Jackie E.; Rich, Patrick; Scherer, Ethan; Burkhauser, Mary A.; Tvedt, Johanna N., 2023, "Replication Data for: Time to Transfer: Long-Term Effects of a Sustained and Spiraled Content Literacy Intervention in the Elementary Grades", https://doi.org/10.7910/DVN/RKBVKY, Harvard Dataverse, V2, UNF:6:8Bs7YiYfLGBG85OmnX3qpw== [fileUNF]
LicenceCC BY-NC-SA 4.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/RKBVKY

Size and shape

Responses49,259
Respondents1,386
Items36
Response categories2
Responses per respondent35.540
Responses per item1368.306
Density0.987
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 nameDomain-Specific (Science) Vocabulary Knowledge

Item text

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

InstrumentVocabulary test following a reading intervention
Mean words per item10.028
Mean characters per item53.111
Mean characters per response6.333
Flesch-Kincaid grade level2.213

Columns

block_idcluster_idcov_black_numcov_hispanic_numcov_iep_numcov_lep_numcov_male_numcov_other_numcov_ses_highcov_ses_lowcov_ses_mediditemrespstd_baselinetottreat

Get the data

Licence: CC BY-NC-SA 4.0 — non-commercial use only; adaptations must be shared under the same licence.

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_7")
# Python
pip install irw

import irw
df = irw.fetch("gilbert_meta_7")

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