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.
| Description | 2 extra contradictory-conspiracy-theory items asked only in Study 2 alongside GCBS |
|---|---|
| Reference | Alsuhibani 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 |
| DOI | 10.1371/journal.pone.0259053 |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.1371/journal.pone.0259053.s003 |
| Responses | 3,016 |
|---|---|
| Respondents | 1,508 |
| Items | 2 |
| Response categories | 5 |
| Responses per respondent | 2 |
| Responses per item | 1,508 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Representative |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Generic Conspiracist Beliefs Scale - contradictory-conspiracy items |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Generic 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 item | 20 |
| Mean characters per item | 120 |
| Mean characters per response | 17.400 |
| Flesch-Kincaid grade level | 11.688 |
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.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
id | IRW 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 |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_age | Covariate | Q1 |
cov_sex | Covariate 1 = Male · 2 = Female | SEX |
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.
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("alsuhibani_2022_gcbs_extra_s2")
# Python
pip install irw
import irw
df = irw.fetch("alsuhibani_2022_gcbs_extra_s2")
@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} }
| IRW version | v557 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v13.0 |
| Redivis dataset DOI | 10.57761/gpg7-sg87 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v40.0 |