idcr_martinez_2023_Story_Recall_3

7,150 responses from 235 respondents to 32 items.

About this table

DescriptionRecall based on shorter stories
ReferenceMartinez, D. (2024). Scoring story recall for individual differences research: Central details, peripheral details, and automated scoring. Behavior Research Methods, 1-17.
DOI10.3758/s13428-024-02480-7
LicenceCC BY 4.0
Source datahttps://osf.io/5qxkh/

Size and shape

Responses7,150
Respondents235
Items32
Response categories2
Responses per respondent30.426
Responses per item223.438
Density0.951
LongitudinalFALSE

Classification

age rangeMixed
sampleGeneral/non-specific
construct typeCognitive/educational
measurement toolTest
item formatConstructed Response
primary language(s)eng
construct nameStory Recall Task — Short Story Version

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

InstrumentRecall based on shorter stories
Mean words per item1.469
Mean characters per item7.781
Flesch-Kincaid grade level39.649

Columns

iditemresp

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python:

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("idcr_martinez_2023_Story_Recall_3")
# Python
pip install irw

import irw
df = irw.fetch("idcr_martinez_2023_Story_Recall_3")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse v53.0
Redivis dataset DOI10.57761/4g08-xt41
Manifest pin for this IRW versionv53.0
Metadata sourceirw_meta v23.0