gilbert_meta_10

96,688 responses from 4,895 respondents to 20 items.

About this table

DescriptionReading self-concept following a literacy intervention
ReferenceKim, J. S., Relyea, J. E., Burkhauser, M. A., Scherer, E., & Rich, P. (2021). Improving elementary grade students’ science and social studies vocabulary knowledge depth, reading comprehension, and argumentative writing: A conceptual replication. Educational Psychology Review, 1-30. Kim, James; Relyea, Jackie Eunjung; Burkhauser, Mary A.; Scherer, Ethan; Rich, Patrick, 2021, "Replication Data for: Improving Elementary Grade Students’ Science and Social Studies Vocabulary Knowledge Depth, Reading Comprehension, and Argumentative Writing: A Conceptual Replication", https://doi.org/10.7910/DVN/HQEMN6, Harvard Dataverse, V1, UNF:6:fM1zIBl88IKxt400zYOJag== [fileUNF]
LicenceCC BY-NC-SA 4.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HQEMN6

Size and shape

Responses96,688
Respondents4,895
Items20
Response categories3
Responses per respondent19.752
Responses per item4834.400
Density0.988
LongitudinalFALSE

Classification

age rangeChild (<18y)
child age (for child-focused studies)Child (6-12y)
sampleEducational
construct typeCognitive/educational
measurement toolObservational rating
item formatMixed
primary language(s)eng
construct nameImproving Elementary Grade Students’ Science and Social Studies Vocabulary Knowledge Depth, Reading Comprehension, and Argumentative Writing: A Conceptual Replication

Item text

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

InstrumentReading self-concept following a literacy intervention
Mean words per item7.800
Mean characters per item37.300
Mean characters per response11.567
Flesch-Kincaid grade level1.446

Columns

block_idcluster_idcov_asiancov_blackcov_hispaniccov_iepcov_lepcov_malecov_ses_highcov_ses_lowcov_ses_medcov_whiteiditemrespstd_baselinetreat

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_10")
# Python
pip install irw

import irw
df = irw.fetch("gilbert_meta_10")

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