17,619 responses from 839 respondents to 21 items.
One of 2 tables from the same source; this one measures Health Knowledge Survey (Entertainment-Education RCT). The others are listed under Related tables.
| Description | Health knowledge in an RCT |
|---|---|
| Reference | Carpena, F. (2024). Entertainment-Education for Better Health: Insights from a Field Experiment in India. The Journal of Development Studies, 60(5), 745-762. Carpena, Fenella, 2024, "Replication Data for: Entertainment-Education for Better Health: Insights from a Field Experiment in India", https://doi.org/10.7910/DVN/EJ03JR, Harvard Dataverse, V1, UNF:6:pX0ZhtBxkRpvQC6AOfVg4g== [fileUNF] |
| Licence | CC0 1.0 |
| Source data | https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EJ03JR |
| Responses | 17,619 |
|---|---|
| Respondents | 839 |
| Items | 21 |
| Response categories | 2 |
| Responses per respondent | 21 |
| Responses per item | 839 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | General/non-specific |
| construct type | Cognitive/educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Health Knowledge Survey (Entertainment-Education RCT) |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Health knowledge in an RCT |
|---|---|
| Mean words per item | 10.952 |
| Mean characters per item | 60 |
| Mean characters per response | 22.286 |
| Flesch-Kincaid grade level | 3.971 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, il_hte_37_38.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 | ||
block_id | IRW standard The level above cluster_id — in a randomised design, the block or stratum within which assignment was made (treat varies within a block, and clusters nest in blocks). | neighborhood |
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 |
std_baseline | IRW standard A standardised pre-treatment score, usually the trial's baseline measure. | made in the build script |
treat | IRW standard Treatment group assignment in experimental studies. | made in the build script |
| Respondent covariates | ||
cov_age | Covariate | age |
cov_completed_elementary | Covariate | completed_elementary |
cov_completed_secondary | Covariate | completed_secondary |
cov_female | Covariate | female |
cov_hh_income | Covariate | hhincome |
cov_hh_size | Covariate | hhsize |
cov_hindu | Covariate | hindu |
cov_married | Covariate | married |
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_37")
# Python
pip install irw
import irw
df = irw.fetch("gilbert_meta_37")
@misc{https://doi.org/10.7910/dvn/ej03jr,
doi = {10.7910/DVN/EJ03JR},
url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/EJ03JR},
author = {Carpena, Fenella},
keywords = {Social Sciences},
title = {Replication Data for: Entertainment-Education for Better Health: Insights from a Field Experiment in India},
publisher = {Harvard Dataverse},
year = {2024}
}
| 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 |