goldberg_2018_neo_pir

202,710 responses from 856 respondents to 240 items.

About this table

DescriptionNEO-PI-R, 240 items scored 0-4, from the Eugene-Springfield Community Sample (dataset 3); N = 856 community adults. Items coded neo1..neo240 in inventory order; domain/facet scores in the deposit are dropped. Covariates cov_sex, cov_age, cov_educ, cov_ethnic, cov_employ, cov_marital from the collection's demographics file.
ReferenceGoldberg, L. R. (2018). Eugene-Springfield Community Sample: ( 3) NEO-PI-R (V1). Harvard Dataverse. https://doi.org/10.7910/DVN/HE6LJR
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/HE6LJR

Size and shape

Responses202,710
Respondents856
Items240
Response categories5
Responses per respondent236.811
Responses per item844.625
Density0.987
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleGeneral/non-specific
construct typePersonality
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct nameNEO Personality Inventory-Revised (NEO-PI-R)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, goldberg_2018_neo_pir.py. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.

ColumnWhat it isSource column
IRW standard columns
idIRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).ID
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Respondent covariates
cov_ageCovariate AGE
cov_educCovariate EDUC
cov_employCovariate EMPLOY
cov_ethnicCovariate ETHNIC
cov_maritalCovariate MARITAL
cov_sexCovariate SEX

What item codes and covariate values stand for is in the codebook released with the source data.

Get the data

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

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("goldberg_2018_neo_pir")
# Python
pip install irw

import irw
df = irw.fetch("goldberg_2018_neo_pir")

How to cite

@misc{https://doi.org/10.7910/dvn/he6ljr,
  doi = {10.7910/DVN/HE6LJR},
  url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/HE6LJR},
  author = {Goldberg, Lew},
  keywords = {Social Sciences},
  title = {( 3) NEO-PI-R},
  publisher = {Harvard Dataverse},
  year = {2018}
}

Version and provenance

IRW versionv584
Redivis datasetitem_response_warehouse_5 v8.5
Redivis dataset DOI10.57761/41xy-5k56
Manifest pin for this IRW versionv8.5
Metadata sourceirw_meta v44.0