franco_2024_unfolding

822 responses from 137 respondents to 6 items.

About this table

DescriptionMeasures of beliefs about the intrinsic value of receiving attention from others, consisting of six items generated via ChatGPT’s GPTbinarytree function (Franco & Carvalho, 2023a) arising from unfolding theory applied to attention-seeking tendencies, with items written in Brazilian Portuguese and collected from 137 participants recruited via convenience sampling.
ReferenceFranco, Víthor R., and Lucas de Francisco Carvalho. 2023. “A Tutorial on Unidimensional Unfolding: From Automatic Item Generation to Insightful Inferences.” PsyArXiv. September 14. doi:10.31234/osf.io/5hnkz.
DOI10.31234/osf.io/5hnkz
LicenceCC BY 4.0
Source datahttps://osf.io/pqjhv/files/osfstorage

Size and shape

Responses822
Respondents137
Items6
Response categories6
Responses per respondent6
Responses per item137
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleGeneral/non-specific, Internet-based
construct typePersonality
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)por
construct nameAttention-Seeking (Unidimensional Unfolding via ChatGPT-Generated Items)

Item text

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

InstrumentUnfolding (Coombs) item set on the intrinsic value of receiving attention from others, generated with the GPTbinarytree function of the coombs R package and administered as a ranking task (Franco & Carvalho 2023, 'A Tutorial on Unidimensional Unfolding', PsyArXiv doi:10.31234/osf.io/5hnkz); deposit osf.io/pqjhv
Mean words per item9
Mean characters per item61.333
Mean characters per response10.500
Flesch-Kincaid grade level11.520

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, franco_2024_unfolding.do. 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.`var'
Respondent covariates
cov_ageCovariate age
cov_educationCovariate scholarity
cov_genderCovariate gender
cov_sexCovariate sex

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("franco_2024_unfolding")
# Python
pip install irw

import irw
df = irw.fetch("franco_2024_unfolding")

How to cite

@article{Franco_2023, title={A Tutorial on Unidimensional Unfolding: From Automatic Item Generation to Insightful Inferences}, url={http://dx.doi.org/10.31234/osf.io/5hnkz}, DOI={10.31234/osf.io/5hnkz}, publisher={Center for Open Science}, author={Franco, Víthor Rosa and de Francisco Carvalho, Lucas}, year={2023}, month=sep }

Version and provenance

IRW versionv566
Redivis datasetitem_response_warehouse_2 v33.0
Redivis dataset DOI10.57761/6m04-m698
Manifest pin for this IRW versionv33.0
Metadata sourceirw_meta v43.0