margaretto_2025_translation_study_1_vocabulary_3

About this table

Size and shape

Classification

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 table is in 1 collection. Each membership records how it was decided:

• Reading & vocabulary reading_vocab — by rule

Membership is the IRW’s judgment and is worth checking before you use a collection as a sample. A hand-curated list is a snapshot: it is not re-checked when tables are added, so a table that would qualify may be missing. See Collections.

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.

What item codes and covariate values stand for is in the codebook released with the source data.

Respondent uniqueness. 26% 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

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_1_vocabulary_3")

# Python
pip install irw

import irw
df = irw.fetch("margaretto_2025_translation_study_1_vocabulary_3")

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_1_vocabulary_3, 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 }

Related tables

• margaretto_2025_translation_study_1_generalknowledge — General Knowledge (GK) · 65 items
• margaretto_2025_translation_study_1_lextale — Lextale-Esp (Spanish vocabulary form-recognition) · 90 items
• margaretto_2025_translation_study_1_readingcomprehension — Reading Comprehension (RC) · 42 items
• margaretto_2025_translation_study_1_vocabulary_1 — Student Vocabulary Test 1 (StuVoc1) · 48 items
• margaretto_2025_translation_study_1_vocabulary_2 — Student Vocabulary Test 2 (StuVoc2) · 49 items
• margaretto_2025_translation_study_2_generalknowledge_1 — General Knowledge 1 (GK1) · 55 items
• margaretto_2025_translation_study_2_generalknowledge_2 — General Knowledge 2 (GK2) · 53 items
• margaretto_2025_translation_study_2_lextale — Lextale-Esp (Spanish vocabulary form-recognition) · 90 items
• margaretto_2025_translation_study_2_readingcomprehension — Reading Comprehension (RC) · 42 items
• margaretto_2025_translation_study_2_vocabulary_full — Student Vocabulary Test combined (StuVoc) · 74 items

Version and provenance