meco_l1_comprehension

About this table

Notes

• Item codes are shared only where the question is the same. Only texts 1, 3, 7, 11 and 12 are translations of a common original (the authors' is.matched flag); their questions are text{t}_q{q} across sites. The other 7 texts were written per language, so their items carry a language or text-version prefix (e.g. du_text2_q1) and are answered only by that site's readers: the table is sparse by design, with a median of 45 readers per item. A version is shared across sites or rounds only where the text is identical (no and tr across rounds, en with en_uk, the two Hindi sites except text 5, German wave 2 as ge_v2, sp with sp_ch on texts 2, 4, 5 and 9). Eight wave-1 matched questions that are a different question in that language get site-specific codes (ee_text3_q1, ee_text3_q2, sp_text3_q3, he_text1_q3, he_text12_q4, ru_text12_q4, gr_text12_q2, tr_text12_q3). (irw#2207)
• Pooled from MECO L1's two collection rounds, which are different people at mostly different sites: cov_sample 1 = wave 1 (13 sites), 2 = wave 2 (16 sites). It is not a panel, so the round is cov_sample rather than wave. cov_language is the language and cov_site the release's site code. The 42 wave-1 Norwegian readers answered 47 questions (text 6 question 4 was not administered). (irw#2207)
• Wave 2 published no comprehension-question wording, so for wave-2 sites the equivalence of the matched questions (text{t}_q{q}) is assumed from the authors' design and is unverified. Two wave-2 sites contradict it in their responses and are coded site-specific instead: ru_mo for all 48 items (its per-question accuracy does not match any other site, nor wave-1 Russian on identical texts) and ba for text 11. (irw#2207)

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Classification

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, meco_l1_comprehension.py. 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 one of the 4 READMEs in the source deposit.

Get the data

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("meco_l1_comprehension")

# Python
pip install irw

import irw
df = irw.fetch("meco_l1_comprehension")

How to cite

@article{Siegelman_2022, title={Expanding horizons of cross-linguistic research on reading: The Multilingual Eye-movement Corpus (MECO)}, volume={54}, ISSN={1554-3528}, url={http://dx.doi.org/10.3758/s13428-021-01772-6}, DOI={10.3758/s13428-021-01772-6}, number={6}, journal={Behavior Research Methods}, publisher={Springer Science and Business Media LLC}, author={Siegelman, Noam and Schroeder, Sascha and Acartürk, Cengiz and Ahn, Hee-Don and Alexeeva, Svetlana and Amenta, Simona and Bertram, Raymond and Bonandrini, Rolando and Brysbaert, Marc and Chernova, Daria and Da Fonseca, Sara Maria and Dirix, Nicolas and Duyck, Wouter and Fella, Argyro and Frost, Ram and Gattei, Carolina A. and Kalaitzi, Areti and Kwon, Nayoung and Lõo, Kaidi and Marelli, Marco and Papadopoulos, Timothy C. and Protopapas, Athanassios and Savo, Satu and Shalom, Diego E. and Slioussar, Natalia and Stein, Roni and Sui, Longjiao and Taboh, Analí and Tønnesen, Veronica and Usal, Kerem Alp and Kuperman, Victor}, year={2022}, month=Feb, pages={2843–2863} }

Version and provenance