3,525 responses from 235 respondents to 15 items.
One of 12 tables from the same source; this one measures Continuation of Number Sequences Task. The others are listed under Related tables.
| Description | Continuation of number sequences |
|---|---|
| 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 | 3,525 |
|---|---|
| Respondents | 235 |
| Items | 15 |
| Response categories | 2 |
| Responses per respondent | 15 |
| Responses per item | 235 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Mixed |
|---|---|
| sample | General/non-specific |
| construct type | Cognitive/educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Continuation of Number Sequences Task |
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 (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("idcr_martinez_2023_numSeries")
# Python
pip install irw
import irw
df = irw.fetch("idcr_martinez_2023_numSeries")
@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 | v566 |
|---|---|
| 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 v43.0 |