592 responses from 148 respondents to 4 items.
| Description | Multi-Dimensional Measure of Trust: sincere subscale, human-AI creative collaboration study, 4 items, 0-7, N=148 |
|---|---|
| Reference | Komura, K., & Yamada, S. (2026). Deepening ideas vs. exploring new ones: AI strategy effects in human-AI creative collaboration. PLOS ONE. |
| DOI | 10.1371/journal.pone.0340449 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0340449 |
| Responses | 592 |
|---|---|
| Respondents | 148 |
| Items | 4 |
| Response categories | 7 |
| Responses per respondent | 4 |
| Responses per item | 148 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Internet-based |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | jpn |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Multi-Dimensional Measure of Trust (MDMT), Sincere subscale |
|---|---|
| Mean words per item | 1 |
| Mean characters per item | 2 |
| Mean characters per response | 3.429 |
| Flesch-Kincaid grade level | -3.400 |
cov_agecov_aitypecov_genderiditemresp
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("komura_2026_mdmt_sincere")
# Python
pip install irw
import irw
df = irw.fetch("komura_2026_mdmt_sincere")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v7.0 |
| Redivis dataset DOI | 10.57761/pqqn-pm43 |
| Manifest pin for this IRW version | v7.0 |
| Metadata source | irw_meta v23.0 |