frac20

10,720 responses from 536 respondents to 20 items.

About this table

DescriptionData from fraction subtraction problems.
ReferenceTatsuoka, C. (2002). Data analytic methods for latent partially ordered classification models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51(3), 337-350.
DOI10.1111/1467-9876.00272
LicenceGPL-3.0
Source datahttps://cran.r-project.org/web/packages/GDINA/index.html

Size and shape

Responses10,720
Respondents536
Items20
Response categories2
Responses per respondent20
Responses per item536
Density1
LongitudinalFALSE

Classification

age rangeChild (<18y)
sampleTargeted/specific
construct typeCognitive/educational
measurement toolTest
item formatLikert Scale/selected response
primary language(s)eng
construct namefrac20 (Fraction Subtraction Data)

Item text

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

InstrumentData from fraction subtraction problems.
Mean words per item4.100
Mean characters per item11.050
Flesch-Kincaid grade level-1.879

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, frac20.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.

ColumnWhat it isSource column
IRW standard columns
idIRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).made in the build script
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Other columns
qmatrix__1Not documented Item classifications for cognitive diagnostic modeling (Q-matrix).not traced
qmatrix__2Not documented Item classifications for cognitive diagnostic modeling (Q-matrix).not traced
qmatrix__3Not documented Item classifications for cognitive diagnostic modeling (Q-matrix).not traced
qmatrix__4Not documented Item classifications for cognitive diagnostic modeling (Q-matrix).not traced
qmatrix__5Not documented Item classifications for cognitive diagnostic modeling (Q-matrix).not traced
qmatrix__6Not documented Item classifications for cognitive diagnostic modeling (Q-matrix).not traced
qmatrix__7Not documented Item classifications for cognitive diagnostic modeling (Q-matrix).not traced
qmatrix__8Not documented Item classifications for cognitive diagnostic modeling (Q-matrix).not traced

What item codes and covariate values stand for is in the codebook released with the source data.

Get the data

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

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("frac20")
# Python
pip install irw

import irw
df = irw.fetch("frac20")

How to cite

@article{Tatsuoka_2002, title={Data Analytic Methods for Latent Partially Ordered Classification Models}, volume={51}, ISSN={1467-9876}, url={http://dx.doi.org/10.1111/1467-9876.00272}, DOI={10.1111/1467-9876.00272}, number={3}, journal={Journal of the Royal Statistical Society Series C: Applied Statistics}, publisher={Oxford University Press (OUP)}, author={Tatsuoka, Curtis}, year={2002}, month=jul, pages={337–350} }

Version and provenance

IRW versionv566
Redivis datasetitem_response_warehouse v70.0
Redivis dataset DOI10.57761/4qxs-pq87
Manifest pin for this IRW versionv70.0
Metadata sourceirw_meta v43.0