1,482 responses from 327 respondents to 5 items.
One of 2 tables from the same source; this one is the compare table. The others are listed under Related tables.
| Description | 5-item comparative trust scale (AI vs. other) across domains (self-driving, medical, historical recall, logistics, personal data), same survey as alasmari_2025_ai_trust_confidence |
|---|---|
| Reference | Alasmari AA, Alruwaili RF, Alotaibi RF, Youssef IK, Asklany SA (2025) Demographic influences on trust in artificial intelligence across cognitive domains: A statistical perspective. PLoS ONE 20(11): e0331003. |
| DOI | 10.1371/journal.pone.0331003 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0331003.s001&type=supplementary |
| Responses | 1,482 |
|---|---|
| Respondents | 327 |
| Items | 5 |
| Response categories | 3 |
| Responses per respondent | 4.532 |
| Responses per item | 296.400 |
| Density | 0.906 |
| Longitudinal | FALSE |
| sample | Educational |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | ara |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | AI vs. Human Trust Comparison Scenarios (Alasmari et al. 2025 survey, Part 4) |
|---|---|
| Mean words per item | 28.200 |
| Mean characters per item | 163.800 |
| Mean characters per response | 16.333 |
| Flesch-Kincaid grade level | 14.349 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, alasmari_2025_ai_trust_compare.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 | Age |
cov_ai_familiarity | Covariate | Familiarty |
cov_ai_use_frequency | Covariate | Frequent use |
cov_gender | Covariate | Gender |
What item codes and covariate 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("alasmari_2025_ai_trust_compare")
# Python
pip install irw
import irw
df = irw.fetch("alasmari_2025_ai_trust_compare")
@article{Alasmari_2025, title={Demographic influences on trust in artificial intelligence across cognitive domains: A statistical perspective}, volume={20}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0331003}, DOI={10.1371/journal.pone.0331003}, number={11}, journal={PLOS One}, publisher={Public Library of Science (PLoS)}, author={Alasmari, Abdullah A. and Alruwaili, Reshaa F. and Alotaibi, Rasmiah F. and Youssef, Ismail K. and Asklany, Somia A.}, editor={Kim, Cheong}, year={2025}, month=Nov, pages={e0331003} }
| IRW version | v549 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v13.0 |
| Redivis dataset DOI | 10.57761/w8zx-td92 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v39.0 |