gilbert_meta_86

27,210 responses from 1,244 respondents to 15 items.

About this table

Descriptionpolitical trust
ReferenceTrexler, Andrew, 2025, "Replication Data for: "The Minimal Effects of Making Local News Free: Evidence from a Field Experiment"", https://doi.org/10.7910/DVN/NWKSHA, Harvard Dataverse, V1, UNF:6:A5Mj0NI8CIQ9vOTFJjRjNw== [fileUNF] Trexler, A. (2023, April 18). The Minimal Effects of Making Local News Free: Evidence from a Field Experiment. https://doi.org/10.31219/osf.io/8x46u
DOI10.31219/osf.io/8x46u
LicenceCC BY 4.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/NWKSHA

Size and shape

Responses27,210
Respondents1,244
Items15
Response categories6
Responses per respondent21.873
Responses per item1,814
Density1.458
LongitudinalTRUE

Classification

age rangeAdult (18+)
primary language(s)no access to the osf page
construct namepolitical trust

Item text

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

Instrumentpolitical trust
Mean words per item12.800
Mean characters per item78.933
Mean characters per response16.833
Flesch-Kincaid grade level8.941

Columns

cov_agecov_asiancov_blackcov_educcov_hispaniccov_malecov_middle_easterncov_multiracialcov_nativecov_whiteiditemresptreatwave

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python:

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("gilbert_meta_86")
# Python
pip install irw

import irw
df = irw.fetch("gilbert_meta_86")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse v53.0
Redivis dataset DOI10.57761/4g08-xt41
Manifest pin for this IRW versionv53.0
Metadata sourceirw_meta v23.0