eammi_grahe_2018_moa2

63,075 responses from 3,180 respondents to 20 items.

About this table

DescriptionMarkers of Adulthood (Achievement)
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

Responses63,075
Respondents3,180
Items20
Response categories4
Responses per respondent19.835
Responses per item3153.750
Density0.992
LongitudinalTRUE

Classification

age rangeAdult (18+)
sampleGeneral/non-specific
construct typeDevelopmental
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct nameMarkers of Adulthood (Achievement)

Item text

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

InstrumentMarkers of Adulthood (Achievement)
Mean words per item5.800
Mean characters per item40.300
Mean characters per response5.571
Flesch-Kincaid grade level10.743

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_moa2")
# Python
pip install irw

import irw
df = irw.fetch("eammi_grahe_2018_moa2")

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