45,066 responses from 1,194 respondents to 38 items.
One of 8 tables from the same source; this one measures Trial-level decision-making responses on the Adult Decision-Making Competence. The others are listed under Related tables.
| Description | This dataset measures trial-level decision-making responses on the Adult Decision-Making Competence (ADMC) battery, including Resistance to Framing, Deliberation/Reflection, and Risk Perception. |
|---|---|
| Reference | Himmelstein, Mark, Sophie Ma Zhu, Nikolay Petrov, Ezra Karger, Jessica Helmer, Sivan Livnat, Amory Bennett, Page Hedley, and Phil Tetlock. “The Forecasting Proficiency Test: A General Use Assessment of Forecasting Ability”. PsyArXiv, November 23, 2025. doi:10.31234/osf.io/a7kdx_v8. |
| DOI | 10.31234/osf.io/a7kdx_v8 |
| Licence | CC BY 4.0 |
| Source data | https://github.com/forecastingresearch/fpt/tree/main/data_cognitive_tasks |
| Responses | 45,066 |
|---|---|
| Respondents | 1,194 |
| Items | 38 |
| Response categories | 7 |
| Responses per respondent | 37.744 |
| Responses per item | 1185.947 |
| Density | 0.993 |
| Longitudinal | TRUE |
| age range | Adult (18+) |
|---|---|
| sample | Targeted/specific |
| construct type | Behavioral |
| measurement tool | Survey/questionnaire |
| item format | Slider/continuous |
| primary language(s) | eng |
| construct name | Trial-level decision-making responses on the Adult Decision-Making Competence |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Adult Decision Making Competence (ADMC): the Resistance to Framing and Applying Decision Rules subtests, administered as part of the Forecasting Proficiency Test battery. |
|---|---|
| Mean words per item | 69.244 |
| Mean characters per item | 391.021 |
| Mean characters per response | 6.788 |
| Flesch-Kincaid grade level | 8.695 |
This codebook is reconstructed by the IRW from the IRW data standard. 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). | not traced |
item | IRW standard Item identifier. | not traced |
resp | IRW standard Response value. | not traced |
rt | IRW standard Response time. | not traced |
wave | IRW standard Longitudinal wave indicator. | not traced |
| Respondent covariates | ||
cov_session_id | Covariate | not traced |
cov_session_restart_id | Covariate | not traced |
What item codes and covariate values stand for is in the source’s documentation, data_admc_raw.docx.
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("himmelstein-admc_raw-2025")
# Python
pip install irw
import irw
df = irw.fetch("himmelstein-admc_raw-2025")
@article{Himmelstein_2025, title={The Forecasting Proficiency Test: A General Use Assessment of Forecasting Ability}, url={http://dx.doi.org/10.31234/osf.io/a7kdx_v8}, DOI={10.31234/osf.io/a7kdx_v8}, publisher={Center for Open Science}, author={Himmelstein, Mark and Zhu, Sophie Ma and Petrov, Nikolay and Karger, Ezra and Helmer, Jessica and Livnat, Sivan and Bennett, Amory and Hedley, Page and Tetlock, Phil}, year={2025}, month=nov }
| IRW version | v557 |
|---|---|
| Redivis dataset | item_response_warehouse_2 v33.0 |
| Redivis dataset DOI | 10.57761/6m04-m698 |
| Manifest pin for this IRW version | v33.0 |
| Metadata source | irw_meta v40.0 |