merlo2025_eng_agentic

10,650 responses from 1,065 respondents to 10 items.

One of 5 tables from the same source; this one is the eng_agentic table. The others are listed under Related tables.

About this table

DescriptionStudent Engagement — Agentic subscale (10 items); Italian university students; N=1065; 1-7 Likert
ReferenceMerlo, G., Jaforte, L., Vassallo, M., & Pozzetti, I. (2025). Dataset for: Sleep Quality as a Mediator Between Lifestyle and Cognitive and Agentic Student Engagement [Dataset]. figshare. https://doi.org/10.6084/m9.figshare.30195541.v1
LicenceCC BY 4.0
Source datahttps://figshare.com/articles/dataset/Dataset_for_Sleep_Quality_as_a_Mediator_Between_Lifestyle_and_Cognitive_and_Agentic_Student_Engagement_A_Structural_Equation_Modeling_Approach/30195541

Size and shape

Responses10,650
Respondents1,065
Items10
Response categories7
Responses per respondent10
Responses per item1,065
Density1
LongitudinalFALSE

Classification

age rangeMixed
child age (for child-focused studies)Adolescent (12-18y)
sampleEducational
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)ita

Codebook

This codebook is reconstructed by the IRW from the IRW data standard. 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).not traced
itemIRW standard Item identifier.not traced
respIRW standard Response value.not traced
Respondent covariates
cov_ageCovariate not traced
cov_heightCovariate not traced
cov_sexCovariate not traced
cov_weightCovariate 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:

# R (no account needed)
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("merlo2025_eng_agentic")
# Python (needs a free Redivis account)
pip install irw

import irw
df = irw.fetch("merlo2025_eng_agentic")

How to cite

@misc{https://doi.org/10.6084/m9.figshare.30195541.v1,
  doi = {10.6084/M9.FIGSHARE.30195541.V1},
  url = {https://figshare.com/articles/dataset/Dataset_for_Sleep_Quality_as_a_Mediator_Between_Lifestyle_and_Cognitive_and_Agentic_Student_Engagement_A_Structural_Equation_Modeling_Approach/30195541/1},
  author = {Merlo, Gianluca and Jaforte, Luna and Vassallo, Massimiliano and Pozzetti, Ilaria},
  keywords = {Other education not elsewhere classified},
  title = {Dataset for: Sleep Quality as a Mediator Between Lifestyle and Cognitive and Agentic Student Engagement: A Structural Equation Modeling Approach},
  publisher = {figshare},
  year = {2025},
  copyright = {Creative Commons Attribution 4.0 International}
}

Version and provenance

IRW versionv540
Redivis datasetitem_response_warehouse_2 v33.0
Redivis dataset DOI10.57761/6m04-m698
Manifest pin for this IRW versionv33.0
Metadata sourceirw_meta v37.0