226,809 responses from 7,797 respondents to 30 items.
| Description | Reading comprehension data from an RCT |
|---|---|
| Reference | Gilbert, J. B., Kim, J. S., & Miratrix, L. W. (2023). Modeling item-level heterogeneous treatment effects with the explanatory item response model: Leveraging large-scale online assessments to pinpoint the impact of educational interventions. Journal of Educational and Behavioral Statistics, 48(6), 889-913. Gilbert, Josh, 2023, "Replication Data for: Modeling Item-Level Heterogeneous Treatment Effects With the Explanatory Item Response Model: Leveraging Large-Scale Online Assessments to Pinpoint the Impact of Educational Interventions", https://doi.org/10.7910/DVN/QARRYT, Harvard Dataverse, V2, UNF:6:r+hUHhEPWWtUbcqXyELTJw== [fileUNF] |
| Licence | CC BY-NC-SA 4.0 |
| Source data | https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QARRYT |
| Responses | 226,809 |
|---|---|
| Respondents | 7,797 |
| Items | 30 |
| Response categories | 2 |
| Responses per respondent | 29.089 |
| Responses per item | 7560.300 |
| Density | 0.970 |
| Longitudinal | FALSE |
| age range | Child (<18y) |
|---|---|
| child age (for child-focused studies) | Child (6-12y) |
| sample | Educational |
| construct type | Cognitive/educational |
| measurement tool | Observational rating |
| item format | Mixed |
| primary language(s) | bra |
| construct name | Modeling Item-Level Heterogeneous Treatment Effects With the Explanatory Item Response Model: Leveraging Large-Scale Online Assessments to Pinpoint the Impact of Educational Interventions |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Reading comprehension data from an RCT |
|---|---|
| Mean words per item | 9.500 |
| Mean characters per item | 54.700 |
| Mean characters per response | 40.408 |
| Flesch-Kincaid grade level | 6.457 |
cluster_ididitemrespstd_baselinetreat
Licence: CC BY-NC-SA 4.0 — non-commercial use only; adaptations must be shared under the same licence.
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("gilbert_meta_1")
# Python
pip install irw
import irw
df = irw.fetch("gilbert_meta_1")
| 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 |