592 responses from 148 respondents to 4 items.
One of 9 tables from the same source; this one is the mdmt_reliable table. The others are listed under Related tables.
| Description | Multi-Dimensional Measure of Trust: reliable 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 | 8 |
| 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), Reliable subscale |
|---|---|
| Mean words per item | 1 |
| Mean characters per item | 2 |
| Mean characters per response | 4.250 |
| Flesch-Kincaid grade level | -3.400 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, komura_2026_godspeed.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). | sessionId |
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 | participantInfo_age |
cov_aitype | Covariate | aitype |
cov_gender | Covariate | participantInfo_gender |
What item codes and covariate values stand for is in the source’s questionnaire, S2_File.docx, which shows the item wording.
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("komura_2026_mdmt_reliable")
# Python
pip install irw
import irw
df = irw.fetch("komura_2026_mdmt_reliable")
@article{Komura_2026, title={Deepening ideas vs. exploring new ones: AI strategy effects in human-AI creative collaboration}, volume={21}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0340449}, DOI={10.1371/journal.pone.0340449}, number={1}, journal={PLOS One}, publisher={Public Library of Science (PLoS)}, author={Komura, Kazuki and Yamada, Seiji}, editor={Piwowar-Sulej, Katarzyna}, year={2026}, month=Jan, pages={e0340449} }
| IRW version | v549 |
|---|---|
| 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 v39.0 |