822 responses from 137 respondents to 6 items.
| Description | Measures 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. |
|---|---|
| Reference | Franco, 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. |
| DOI | 10.31234/osf.io/5hnkz |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/pqjhv/files/osfstorage |
| Responses | 822 |
|---|---|
| Respondents | 137 |
| Items | 6 |
| Response categories | 6 |
| Responses per respondent | 6 |
| Responses per item | 137 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | General/non-specific, Internet-based |
| construct type | Personality |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | por |
| construct name | Attention-Seeking (Unidimensional Unfolding via ChatGPT-Generated Items) |
cov_agecov_educationcov_gendercov_sexiditemresp
Or load it directly in R or Python:
# 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")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse_2 v19.0 |
| Redivis dataset DOI | 10.57761/qtx5-4m80 |
| Manifest pin for this IRW version | v19.0 |
| Metadata source | irw_meta v23.0 |