alsuhibani_2022_npi_s3

About this table

Notes

• Item text: item_text is NA for all 13 items, and that is correct. The NPI-13 is forced-choice, with no item stem; each item is a pair of statements, and both are in option_text (one per resp value). (irw#1649)

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Classification

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.

This table is in 1 collection. Each membership records how it was decided:

• Dark Triad dark_triad — by rule

Membership is the IRW’s judgment and is worth checking before you use a collection as a sample. A hand-curated list is a snapshot: it is not re-checked when tables are added, so a table that would qualify may be missing. See Collections.

Item text

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

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.

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. 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, and on a sample this size it is largely arithmetic. The IRW republishes the demographics its sources published and never at finer granularity: see the data standard.

Get the data

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_npi_s3")

# Python
pip install irw

import irw
df = irw.fetch("alsuhibani_2022_npi_s3")

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_npi_s3, 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} }

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Version and provenance