gilbert_meta_56

17,364 responses from 2,712 respondents to 4 items.

About this table

DescriptionLiteracy in an RCT
Referencehttps://riseprogramme.org/sites/default/files/inline-files/Rudasingwa_Improving_Reading_Proficiency_ECE_Liberia.pdf Sebele, Michael; McManus, Jeffery; Rudasingwa, Mico, 2023, "Replication Data for: Improving reading proficiency in early childhood education classrooms, Evidence from Liberia", https://doi.org/10.7910/DVN/ZSRO4X, Harvard Dataverse, V1, UNF:6:9noRP6CDtDIYblAyUBX+7A== [fileUNF]
LicenceCC0 1.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZSRO4X

Size and shape

Responses17,364
Respondents2,712
Items4
Response categories2
Responses per respondent6.403
Responses per item4,341
Density1.601
LongitudinalTRUE

Classification

age rangeChild (<18y)
child age (for child-focused studies)Child (6-12y), Adolescent (12-18y)
sampleEducational
construct typeCognitive/educational
measurement toolTest
item formatConstructed Response
primary language(s)eng
construct nameReading assessment

Item text

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

InstrumentLiteracy in an RCT
Mean words per item30.250
Mean characters per item145.500
Mean characters per response2
Flesch-Kincaid grade level1.108

Columns

block_idcluster_idcov_agecov_maleiditemresptottreatwave

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_56")
# Python
pip install irw

import irw
df = irw.fetch("gilbert_meta_56")

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