44,999,610 responses from 1,000,000 respondents to 45 items.
The scored version of this table is enem_2016_1mil_mt.
One of 104 tables from the same source; this one measures ENEM Mathematics Proficiency Assessment (2016). The others are listed under Related tables.
| Description | Nominal version of enem_2016_1mil_mt: text is the response option the examinee marked on the mathematics portion of the 2016 Brazilian ENEM. resp is retained as the scored 0/1. |
|---|---|
| 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 | 44,999,610 |
|---|---|
| Respondents | 1,000,000 |
| Items | 45 |
| Response categories | 7 |
| Responses per respondent | 45.000 |
| 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 Mathematics Proficiency Assessment (2016) |
bookletiditempositionresptext
id, item, resp are defined in the IRW data standard and mean the same in every table. For the other columns, including what item codes and covariate values stand for, see the source’s codebook, Dicionário_Microdados_Enem_2016.xlsx (inside microdados_enem_2016.zip, 1 GB).
This table is larger than Redivis serves as a CSV without a login. The R package below downloads it with no account; the Python package and the Redivis website need you to sign in to Redivis.
Or load it directly in R or Python:
# R (no account needed)
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("enem_2016_1mil_mt_nom", source = "nom")
# Python (needs a free Redivis account)
pip install irw
import irw
df = irw.fetch("enem_2016_1mil_mt_nom", source="nom")
@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=Sept, pages={2898–2907} }
| IRW version | v540 |
|---|---|
| Redivis dataset | irw_nominal v4.1 |
| Redivis dataset DOI | 10.57761/9hzd-dc70 |
| Manifest pin for this IRW version | v4.0 |
| Metadata source | irw_meta v37.0 |