goldberg_2018_pea_belief

22,429 responses from 749 respondents to 30 items.

One of 7 tables from the same source; this one measures Magical / paranormal belief statements (ESCS PEA). The others are listed under Related tables.

About this table

DescriptionESCS dataset 21 belief block: 30 magical/paranormal-belief statements (belief01-belief30), 1 strongly disagree to 5 strongly agree; N = 749. ESCS demographic covariates.
ReferenceGoldberg, L. R. (2018). Eugene-Springfield Community Sample: (21) Personality, Emotions, and Attitudes (PEA) (V1). Harvard Dataverse. https://doi.org/10.7910/DVN/NWYX4N
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/NWYX4N

Size and shape

Responses22,429
Respondents749
Items30
Response categories5
Responses per respondent29.945
Responses per item747.633
Density0.998
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleGeneral/non-specific
construct typeOpinion/attitude
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct nameMagical / paranormal belief statements (ESCS PEA)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, goldberg_2018_pea.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_pea_belief")
# Python
pip install irw

import irw
df = irw.fetch("goldberg_2018_pea_belief")

How to cite

@misc{https://doi.org/10.7910/dvn/nwyx4n,
  doi = {10.7910/DVN/NWYX4N},
  url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/NWYX4N},
  author = {Goldberg, Lewis R.},
  keywords = {Social Sciences},
  title = {(21) Personality, Emotions, and Attitudes (PEA)},
  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