1,482 responses from 327 respondents to 5 items.
| 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 |
cov_agecov_ai_familiaritycov_ai_use_frequencycov_genderiditemresp
Or load it directly in R or Python:
# 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")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v7.0 |
| Redivis dataset DOI | 10.57761/cpj5-vm97 |
| Manifest pin for this IRW version | v7.0 |
| Metadata source | irw_meta v23.0 |