66,416 responses from 1,186 respondents to 56 items.
One of 8 tables from the same source; this one measures Abstract reasoning with the Shipley Institute of Living Scale. The others are listed under Related tables.
| Description | This dataset measures abstract reasoning with the Shipley Institute of Living Scale. |
|---|---|
| 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 | 66,416 |
|---|---|
| Respondents | 1,186 |
| Items | 56 |
| Response categories | 2 |
| Responses per respondent | 56 |
| Responses per item | 1,186 |
| Density | 1 |
| Longitudinal | TRUE |
| age range | Adult (18+) |
|---|---|
| construct type | Cognitive/educational |
| measurement tool | Test |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Abstract reasoning with the Shipley Institute of Living Scale |
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 |
wave | IRW standard Longitudinal wave indicator. | not traced |
| Respondent covariates | ||
cov_session_id | Covariate | not traced |
What item codes and covariate values stand for is in the source’s documentation, data_shipley_abstraction.docx.
Or load it directly in R or Python:
# R (no account needed)
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("himmelstein-shipley_abstraction-2025")
# Python (needs a free Redivis account)
pip install irw
import irw
df = irw.fetch("himmelstein-shipley_abstraction-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 | v540 |
|---|---|
| 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 v37.0 |