25,416 responses from 2,118 respondents to 12 items.
| Description | Vocabulary test |
|---|---|
| Reference | Gilbert, J. B., Domingue, B. W., & Kim, J. S. (2025). Estimating causal effects on psychological networks using item response theory. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000764 |
| DOI | 10.1037/met0000764 |
| Licence | CC BY-NC-SA 4.0 |
| Source data | https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CVO9EZ |
| Responses | 25,416 |
|---|---|
| Respondents | 2,118 |
| Items | 12 |
| Response categories | 2 |
| Responses per respondent | 12 |
| Responses per item | 2,118 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Non-human |
|---|---|
| sample | General/non-specific |
| construct type | Other |
| measurement tool | Observational rating |
| item format | Mixed |
| primary language(s) | eng |
| construct name | Estimating Causal Effects on Psychological Networks Using Item Response Theory |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Vocabulary test |
|---|---|
| Mean words per item | 7 |
| Mean characters per item | 38.667 |
| Mean characters per response | 6.417 |
| Flesch-Kincaid grade level | 3.576 |
block_idcluster_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_100")
# Python
pip install irw
import irw
df = irw.fetch("gilbert_meta_100")
| 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 |