alsuhibani_2022_gcbs_extra_s2

3,016 responses from 1,508 respondents to 2 items.

One of 10 tables from the same source; this one measures Generic Conspiracist Beliefs Scale - contradictory-conspiracy items. The others are listed under Related tables.

About this table

Description2 extra contradictory-conspiracy-theory items asked only in Study 2 alongside GCBS
ReferenceAlsuhibani A, Shevlin M, Freeman D, Sheaves B, Bentall RP (2022). Why conspiracy theorists are not always paranoid: Conspiracy theories and paranoia form separate factors with distinct psychological predictors. PLOS ONE, 17(4), e0259053. https://doi.org/10.1371/journal.pone.0259053
DOI10.1371/journal.pone.0259053
LicenceCC BY 4.0
Source datahttps://doi.org/10.1371/journal.pone.0259053.s003

Size and shape

Responses3,016
Respondents1,508
Items2
Response categories5
Responses per respondent2
Responses per item1,508
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleRepresentative
construct typeOpinion/attitude
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct nameGeneric Conspiracist Beliefs Scale - contradictory-conspiracy items

These are classifications the IRW records, not the study’s own words. Some were annotated by hand and some written by an automated tagger whose output is published only where it clears a measured accuracy bar; which values came from which is not recorded per table. Coverage and accuracy per column, and what is held back, are on Tag quality.

Item text

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

InstrumentGeneric Conspiracist Beliefs Scale, 17-item Study 2 administration (15 GCBS items plus two contradictory Princess Diana items after Wood et al. 2012): source items GCBS16-17
Mean words per item20
Mean characters per item120
Mean characters per response17.400
Flesch-Kincaid grade level12.268

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, alsuhibani_2022_conspiracy_paranoia.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).made in the build script
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Respondent covariates
cov_ageCovariate Q1
cov_sexCovariate 1 = Male · 2 = FemaleSEX

Covariate value labels are the source’s own, as the IRW recorded them. What item codes and any unlabelled values stand for is in the codebook released with the source data.

Respondent uniqueness. Under 1% of this table’s respondents are alone in their combination of the 2 covariates above — no one else in the table shares it. That is a necessary condition for re-identification and not re-identification itself. The IRW republishes the demographics its sources published and never at finer granularity: see the data standard.

Get the data

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

Or load it directly in R (no account needed with irw 1.4.0 or later) or Python (needs a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("irw")
library(irw)
df <- irw_fetch("alsuhibani_2022_gcbs_extra_s2")
# Python
pip install irw

import irw
df = irw.fetch("alsuhibani_2022_gcbs_extra_s2")

Or in Stata, which reads the same no-account CSV as the button above:

* Stata
net install irw, from("https://raw.githubusercontent.com/itemresponsewarehouse/Stata-pkg/main/")

irw_fetch alsuhibani_2022_gcbs_extra_s2, clear

How to cite

@article{Alsuhibani_2022, title={Why conspiracy theorists are not always paranoid: Conspiracy theories and paranoia form separate factors with distinct psychological predictors}, volume={17}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0259053}, DOI={10.1371/journal.pone.0259053}, number={4}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Alsuhibani, Azzam and Shevlin, Mark and Freeman, Daniel and Sheaves, Bryony and Bentall, Richard P.}, editor={Jonason, Peter Karl}, year={2022}, month=Apr, pages={e0259053} }

Version and provenance

IRW versionv674
Redivis datasetitem_response_warehouse_3 v15.0
Redivis dataset DOI10.57761/6rg4-6f73
Manifest pin for this IRW versionv15.0
Metadata sourceirw_meta v59.0