1,472,429 responses from 78,896 respondents to 25 items.
| Description | Corruption Perception |
|---|---|
| Reference | INEGI. Encuesta Nacional de Calidad e Impacto Gubernamental 2023. SNIEG. Información de Interés Nacional |
| Licence | Permission via Email |
| Source data | https://www.inegi.org.mx/programas/encig/2023/#microdatos |
| Responses | 1,472,429 |
|---|---|
| Respondents | 78,896 |
| Items | 25 |
| Response categories | 4 |
| Responses per respondent | 18.663 |
| Responses per item | 58897.160 |
| Density | 0.747 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | General/non-specific |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | spa |
| construct name | Corruption Perception Index |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Corruption Perception |
|---|---|
| Mean words per item | 4.160 |
| Mean characters per item | 31.720 |
| Mean characters per response | 12.480 |
| Flesch-Kincaid grade level | 11.226 |
cov_agecov_educationcov_hhsizecov_sexiditemresp
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:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("mexico_2023_quality_corruptionperception")
# Python
pip install irw
import irw
df = irw.fetch("mexico_2023_quality_corruptionperception")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse_2 v19.0 |
| Redivis dataset DOI | 10.57761/qtx5-4m80 |
| Manifest pin for this IRW version | v19.0 |
| Metadata source | irw_meta v23.0 |