40,000,000 responses from 1,000,000 respondents to 84 items.
A version of this table that keeps the option each respondent chose is enem_2022_1mil_lc_nom.
One of 104 tables from the same source; this one measures ENEM Language Proficiency Assessment (2022). The others are listed under Related tables.
| Description | Language and codes portion of the 2022 Brazilian ENEM, one-million-person subsample, restricted to main-application items only (issue #955). Binary scored (0/1). LC keeps BOTH languages: each student answered only English OR Spanish foreign-language items, resolved via TP_LINGUA (sparse by design, density ~0.9 -- issue #723). |
|---|---|
| Reference | Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907. |
| DOI | 10.46814/lajdv3n5-018 |
| Licence | CC BY 4.0 |
| Source data | https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem |
| Responses | 40,000,000 |
|---|---|
| Respondents | 1,000,000 |
| Items | 84 |
| Response categories | 2 |
| Responses per respondent | 40 |
| Responses per item | 476190.476 |
| Density | 0.476 |
| Longitudinal | FALSE |
| age range | Child (<18y) |
|---|---|
| child age (for child-focused studies) | Adolescent (12-18y) |
| sample | Representative |
| construct type | Cognitive/educational |
| measurement tool | Test |
| item format | Likert Scale/selected response |
| primary language(s) | por |
| construct name | ENEM Language Proficiency Assessment (2022) |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | ENEM 2022 - LC |
|---|---|
| Mean words per item | 1 |
| Mean characters per item | 2 |
| Mean characters per response | 56.704 |
| Flesch-Kincaid grade level | -3.400 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, enem_2022.R. 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). | NU_INSCRICAO |
item | IRW standard Item identifier. | CO_ITEM |
resp | IRW standard Response value. | made in the build script |
| Other columns | ||
booklet | From source Not defined by the IRW standard; the source column’s codebook entry gives its meaning. | CO_PROVA |
position | From source Not defined by the IRW standard; the source column’s codebook entry gives its meaning. | CO_POSICAO |
What item codes and covariate values stand for is in the source’s codebook, Dicionário_Microdados_Enem_2022.xlsx (inside microdados_enem_2022.zip, 621 MB).
This table is larger than Redivis serves without a login, so download it with one of the packages below or while signed in to Redivis.
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("enem_2022_1mil_lc")
# Python
pip install irw
import irw
df = irw.fetch("enem_2022_1mil_lc")
@article{Martins_2021, title={Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil}, volume={3}, ISSN={2674-9297}, url={http://dx.doi.org/10.46814/lajdv3n5-018}, DOI={10.46814/lajdv3n5-018}, number={5}, journal={Latin American Journal of Development}, publisher={South Florida Publishing LLC}, author={Martins, Dayse Marinho}, year={2021}, month=sep, pages={2898–2907} }
| IRW version | v569 |
|---|---|
| Redivis dataset | item_response_warehouse v71.0 |
| Redivis dataset DOI | 10.57761/f5n4-k797 |
| Manifest pin for this IRW version | v70.0 |
| Metadata source | irw_meta v43.0 |