7,150 responses from 235 respondents to 32 items.
One of 12 tables from the same source; this one measures Story Recall Task — Short Story Version. The others are listed under Related tables.
| Description | Recall based on shorter stories |
|---|---|
| Reference | Martinez, D. (2024). Scoring story recall for individual differences research: Central details, peripheral details, and automated scoring. Behavior Research Methods, 1-17. |
| DOI | 10.3758/s13428-024-02480-7 |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/5qxkh/ |
| Responses | 7,150 |
|---|---|
| Respondents | 235 |
| Items | 32 |
| Response categories | 2 |
| Responses per respondent | 30.426 |
| Responses per item | 223.438 |
| Density | 0.951 |
| Longitudinal | FALSE |
| age range | Mixed |
|---|---|
| sample | General/non-specific |
| construct type | Cognitive/educational |
| measurement tool | Test |
| item format | Constructed Response |
| primary language(s) | eng |
| construct name | Story Recall Task — Short Story Version |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Recall based on shorter stories |
|---|---|
| Mean words per item | 1.469 |
| Mean characters per item | 7.781 |
| Flesch-Kincaid grade level | 39.649 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, idcr_martinez_2023.r. 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). | participant |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
What item codes and covariate values stand for is in the codebook released with the source data.
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("idcr_martinez_2023_Story_Recall_3")
# Python (needs a free Redivis account)
pip install irw
import irw
df = irw.fetch("idcr_martinez_2023_Story_Recall_3")
@article{Martinez_2024, title={Scoring story recall for individual differences research: Central details, peripheral details, and automated scoring}, volume={56}, ISSN={1554-3528}, url={http://dx.doi.org/10.3758/s13428-024-02480-7}, DOI={10.3758/s13428-024-02480-7}, number={8}, journal={Behavior Research Methods}, publisher={Springer Science and Business Media LLC}, author={Martinez, David}, year={2024}, month=aug, pages={8362–8378} }
| IRW version | v540 |
|---|---|
| Redivis dataset | item_response_warehouse v70.0 |
| Redivis dataset DOI | 10.57761/4qxs-pq87 |
| Manifest pin for this IRW version | v70.0 |
| Metadata source | irw_meta v37.0 |