11,660 responses from 220 respondents to 55 items.
One of 11 tables from the same source; this one measures General Knowledge 1 (GK1). The others are listed under Related tables.
| Description | Study 2’s first general knowledge test responses |
|---|---|
| Reference | Bermúdez-Margaretto, B., Brysbaert, M. How efficient is translation in language testing? Deriving valid student vocabulary tests in Spanish (StuVoc1-Esp and StuVoc2-Esp) from established English tests. Behav Res 57, 183 (2025). https://doi.org/10.3758/s13428-025-02708-0 |
| DOI | 10.3758/s13428-025-02708-0 |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/c8n2x/ |
| Responses | 11,660 |
|---|---|
| Respondents | 220 |
| Items | 55 |
| Response categories | 2 |
| Responses per respondent | 53 |
| Responses per item | 212 |
| Density | 0.964 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Educational, Targeted/specific |
| construct type | Other |
| measurement tool | Test |
| item format | Likert Scale/selected response |
| primary language(s) | eng, spa |
| construct name | General Knowledge 1 (GK1) |
These are classifications the IRW records, not the study’s own words. Some were annotated by hand and some written by an automated tagger whose output is published only where it clears a measured accuracy bar; which values came from which is not recorded per table. Coverage and accuracy per column, and what is held back, are on Tag quality.
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, margaretto_2025_translation.do. 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 | ||
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). | made in the build script |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | `var' |
| Respondent covariates | ||
cov_age | Covariate | Age |
cov_country | Covariate | Country |
cov_educationlevel | Covariate | Highesteducationlevel |
cov_gender | Covariate | Gender |
cov_motherlanguage | Covariate | Motherlanguage |
cov_multilingual | Covariate | Speaksotherlanguage |
cov_occupation | Covariate | Occupation |
What item codes and covariate values stand for is in the source’s documentation, Survey_3_GeneralKnowledgeTest_1.pdf.
Respondent uniqueness. 55% of this table’s respondents are alone in their combination of the 5 covariates above — no one else in the table shares it. That is a necessary condition for re-identification and not re-identification itself, and on a sample this size it is largely arithmetic. The IRW republishes the demographics its sources published and never at finer granularity: see the data standard.
Or load it directly in R (no account needed with irw 1.4.0 or later) or Python (needs a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("irw")
library(irw)
df <- irw_fetch("margaretto_2025_translation_study_2_generalknowledge_1")
# Python
pip install irw
import irw
df = irw.fetch("margaretto_2025_translation_study_2_generalknowledge_1")
Or in Stata, which reads the same no-account CSV as the button above:
* Stata
net install irw, from("https://raw.githubusercontent.com/itemresponsewarehouse/Stata-pkg/main/")
irw_fetch margaretto_2025_translation_study_2_generalknowledge_1, clear
@article{Berm_dez_Margaretto_2025, title={How efficient is translation in language testing? Deriving valid student vocabulary tests in Spanish (StuVoc1-Esp and StuVoc2-Esp) from established English tests}, volume={57}, ISSN={1554-3528}, url={http://dx.doi.org/10.3758/s13428-025-02708-0}, DOI={10.3758/s13428-025-02708-0}, number={7}, journal={Behavior Research Methods}, publisher={Springer Science and Business Media LLC}, author={Bermúdez-Margaretto, Beatriz and Brysbaert, Marc}, year={2025}, month=may }
| IRW version | v674 |
|---|---|
| Redivis dataset | item_response_warehouse_2 v35.0 |
| Redivis dataset DOI | 10.57761/kpjk-f829 |
| Manifest pin for this IRW version | v35.0 |
| Metadata source | irw_meta v59.0 |