mexico_2023_quality_drainage
384,343 responses from 78,896 respondents to 5 items.
About this table
| Description | Drainage Service Quality |
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| Reference | INEGI. Encuesta Nacional de Calidad e Impacto Gubernamental 2023. SNIEG. Información de Interés Nacional |
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| Licence | Permission via Email |
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| Source data | https://www.inegi.org.mx/programas/encig/2023/#microdatos |
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Size and shape
| Responses | 384,343 |
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| Respondents | 78,896 |
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| Items | 5 |
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| Response categories | 7 |
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| Responses per respondent | 4.872 |
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| Responses per item | 76868.600 |
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| Density | 0.974 |
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| Longitudinal | FALSE |
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Classification
| age range | Adult (18+) |
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| sample | General/non-specific |
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| construct type | Opinion/attitude |
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| measurement tool | Survey/questionnaire |
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| item format | Likert Scale/selected response |
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| primary language(s) | spa |
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| construct name | Drainage Service Quality Evaluation |
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Item text
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Drainage Service Quality |
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| Mean words per item | 15.211 |
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| Mean characters per item | 96.263 |
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| Mean characters per response | 11.737 |
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| Flesch-Kincaid grade level | 10.557 |
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Columns
cov_agecov_educationcov_hhsizecov_sexiditemresp
Get the data
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_drainage")
# Python
pip install irw
import irw
df = irw.fetch("mexico_2023_quality_drainage")
Version and provenance
| IRW version | v393 |
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| Redivis dataset | item_response_warehouse_2 v19.0 |
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| Redivis dataset DOI | 10.57761/qtx5-4m80 |
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| Manifest pin for this IRW version | v19.0 |
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| Metadata source | irw_meta v23.0 |
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