eammi_grahe_2018_belong

31,798 responses from 3,180 respondents to 12 items.

About this table

DescriptionNeed to Belong
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

Responses31,798
Respondents3,180
Items12
Response categories5
Responses per respondent9.999
Responses per item2649.833
Density0.833
LongitudinalTRUE

Classification

age rangeAdult (18+)
sampleGeneral/non-specific
construct typePersonality
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct nameNeed to Belong

Item text

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

InstrumentNeed to Belong
Mean words per item10.917
Mean characters per item53
Mean characters per response7.500
Flesch-Kincaid grade level4.908

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_belong")
# Python
pip install irw

import irw
df = irw.fetch("eammi_grahe_2018_belong")

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