eammi_grahe_2018_transgressions

12,682 responses from 3,175 respondents to 4 items.

About this table

DescriptionInterpresonal Transgressions
ReferenceGrahe, J. E., Chalk, H. M., Cramblet Alvarez, L. D., Faas, C., Hermann, A., Ph.D., McFall, J. P., & Molyneux, K. (2019, June 13). EAMMi2 Public Data. https://doi.org/10.17605/OSF.IO/QTQPB
DOI10.5334/jopd.38
LicencePermission via Email
Source datahttps://osf.io/qtqpb/overview

Size and shape

Responses12,682
Respondents3,175
Items4
Response categories7
Responses per respondent3.994
Responses per item3170.500
Density0.999
LongitudinalTRUE

Classification

age rangeAdult (18+)
sampleGeneral/non-specific
construct typeBehavioral
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct nameInterpresonal Transgressions

Item text

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

InstrumentInterpersonal Transgressions
Mean words per item5.500
Mean characters per item31
Mean characters per response4.286
Flesch-Kincaid grade level63.243

Columns

cov_agecov_armed_forces_yearscov_childhood_statecov_educationcov_gendercov_incomecov_political_ideologycov_political_partycov_president_supportcov_racecov_schoolcov_siblingscov_us_residentcov_years_in_usdateiditemresprt

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("eammi_grahe_2018_transgressions")
# Python
pip install irw

import irw
df = irw.fetch("eammi_grahe_2018_transgressions")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse_2 v19.0
Redivis dataset DOI10.57761/qtx5-4m80
Manifest pin for this IRW versionv19.0
Metadata sourceirw_meta v23.0