margaretto_2025_translation_study_2_generalknowledge_2

11,183 responses from 220 respondents to 53 items.

One of 11 tables from the same source; this one measures General Knowledge 2 (GK2). The others are listed under Related tables.

About this table

DescriptionStudy 2’s second general knowledge test responses
ReferenceBermúdez-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
DOI10.3758/s13428-025-02708-0
LicenceCC BY 4.0
Source datahttps://osf.io/c8n2x/

Size and shape

Responses11,183
Respondents220
Items53
Response categories2
Responses per respondent50.832
Responses per item211
Density0.959
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational, Targeted/specific
construct typeOther
measurement toolTest
item formatLikert Scale/selected response
primary language(s)eng, spa
construct nameGeneral Knowledge 2 (GK2)

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.

Codebook

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.

ColumnWhat it isSource column
IRW standard columns
idIRW 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
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.`var'
Respondent covariates
cov_ageCovariate Age
cov_countryCovariate Country
cov_educationlevelCovariate Highesteducationlevel
cov_genderCovariate Gender
cov_motherlanguageCovariate Motherlanguage
cov_multilingualCovariate Speaksotherlanguage
cov_occupationCovariate Occupation

What item codes and covariate values stand for is in the source’s documentation, Survey_3_GeneralKnowledgeTest_2.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.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

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_2")
# Python
pip install irw

import irw
df = irw.fetch("margaretto_2025_translation_study_2_generalknowledge_2")

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_2, clear

How to cite

@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 }

Version and provenance

IRW versionv674
Redivis datasetitem_response_warehouse_2 v35.0
Redivis dataset DOI10.57761/kpjk-f829
Manifest pin for this IRW versionv35.0
Metadata sourceirw_meta v59.0