gilbert_meta_70

395,462 responses from 6,391 respondents to 33 items.

About this table

DescriptionHindi outcomes in an RCT
ReferenceBanerjee, A., Banerji, R., Berry, J., Duflo, E., Kannan, H., Mukerji, S., ... & Walton, M. (2017). From proof of concept to scalable policies: Challenges and solutions, with an application. Journal of Economic Perspectives, 31(4), 73-102. Banerjee, Abhijit; Banerji, Rukmini; Duflo, Esther; Kannan, Harini; Mukerji, Shobhini; Shotland, Marc; Berry, James; Walton, Michael, 2017, "Raw and Replication Data for: 'From Proof of Concept to Scalable Policies' and 'Mainstreaming an Effective Intervention'", https://doi.org/10.7910/DVN/DUBA3J, Harvard Dataverse, V7, UNF:6:6Ch1ala9EZ8xITxlcjRckg== [fileUNF]
LicenceCC0 1.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DUBA3J

Size and shape

Responses395,462
Respondents6,391
Items33
Response categories2
Responses per respondent61.878
Responses per item11983.697
Density1.875
LongitudinalTRUE

Classification

age rangeChild (<18y)
child age (for child-focused studies)Early (<6y), Child (6-12y), Adolescent (12-18y)

Columns

cluster_idcov_agecov_femalecov_gradee2_std12e2_std35iditemitem_graderesptesttreatwave

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_70")
# Python
pip install irw

import irw
df = irw.fetch("gilbert_meta_70")

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