358,304 responses from 5,386 respondents to 36 items.
| Description | Math outcomes in an RCT |
|---|---|
| Reference | Banerjee, 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] |
| Licence | CC0 1.0 |
| Source data | https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DUBA3J |
| Responses | 358,304 |
|---|---|
| Respondents | 5,386 |
| Items | 36 |
| Response categories | 2 |
| Responses per respondent | 66.525 |
| Responses per item | 9952.889 |
| Density | 1.848 |
| Longitudinal | TRUE |
| age range | Child (<18y) |
|---|---|
| child age (for child-focused studies) | Child (6-12y), Adolescent (12-18y) |
| sample | Educational, Program-based, Targeted/specific |
| construct type | Cognitive/educational |
| measurement tool | Test |
| item format | Likert Scale/selected response |
| primary language(s) | hin |
| construct name | Math performance |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Written mathematics test for standards 3-5 (Banerjee et al. 2017 evaluation) |
|---|---|
| Mean words per item | 2.861 |
| Mean characters per item | 19.611 |
| Mean characters per response | 2 |
| Flesch-Kincaid grade level | 6.163 |
cluster_idcov_agecov_femalecov_gradee2_std12e2_std35iditemitem_graderesptesttreatwave
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("gilbert_meta_73")
# Python
pip install irw
import irw
df = irw.fetch("gilbert_meta_73")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse v53.0 |
| Redivis dataset DOI | 10.57761/4g08-xt41 |
| Manifest pin for this IRW version | v53.0 |
| Metadata source | irw_meta v23.0 |