358,304 responses from 5,386 respondents to 36 items.
One of 6 tables from the same source; this one measures Math performance. The others are listed under Related tables.
| 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 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, il_hte_70_71_72_73.R. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
cluster_id | IRW standard The group a focal unit is nested in — classroom, teacher, school. | villageid |
id | IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task). | not traced |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | not traced |
treat | IRW standard Treatment group assignment in experimental studies. | treatvillage |
wave | IRW standard Longitudinal wave indicator. | not traced |
| Respondent covariates | ||
cov_age | Covariate | age_year |
cov_female | Covariate | female |
cov_grade | Covariate | base_standard |
| Other columns | ||
e2_std12 | Not documented Not documented by the IRW; see the source’s codebook. | made in the build script |
e2_std35 | Not documented Not documented by the IRW; see the source’s codebook. | made in the build script |
item_grade | Not documented Not documented by the IRW; see the source’s codebook. | made in the build script |
test | Not documented Not documented by the IRW; see the source’s codebook. | made in the build script |
What item codes and covariate values stand for is in the codebook released with the source data.
Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):
# 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")
@misc{https://doi.org/10.7910/dvn/duba3j,
doi = {10.7910/DVN/DUBA3J},
url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/DUBA3J},
author = {Banerjee, Abhijit and Banerji, Rukmini and Duflo, Esther and Kannan, Harini and Mukerji, Shobhini and Shotland, Marc and Berry, James and Walton, Michael},
keywords = {Social Sciences},
title = {Raw and Replication Data for: 'From Proof of Concept to Scalable Policies' and 'Mainstreaming an Effective Intervention'},
publisher = {Harvard Dataverse},
year = {2017}
}
| IRW version | v566 |
|---|---|
| Redivis dataset | item_response_warehouse v70.0 |
| Redivis dataset DOI | 10.57761/4qxs-pq87 |
| Manifest pin for this IRW version | v70.0 |
| Metadata source | irw_meta v43.0 |