QaSLU_Prado_2024

5,310 responses from 118 respondents to 45 items.

About this table

DescriptionQuestionnaire for the Self-assessment of University Service-learning Experiences(QaSLU)
ReferenceLópez-de-Arana Prado, E., Aramburuzabala, P., Cerrillo, R., & Sánchez-Cabrero, R. (2024). Validation and Standardization of a Questionnaire for the Self-Assessment of Service-Learning Experiences in Higher Education (QaSLu-27). Education Sciences, 14(6), 615. López de Arana Prado, Elena; Sánchez-Cabrero, Roberto; Aramburuzabala, Pilar; Cerrillo, Rosario, 2024, "Dataset of the initial validation of Questionnaire for the Self-assessment of University Service-learning Experiences (QaSLu)", https://doi.org/10.7910/DVN/LUUDQC, Harvard Dataverse, V3, UNF:6:0G58Cn2FWIOPS6cx7++mzg== [fileUNF]
LicenceCC0 1.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LUUDQC

Size and shape

Responses5,310
Respondents118
Items45
Response categories5
Responses per respondent45
Responses per item118
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational, Targeted/specific
construct typeCognitive/educational
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct nameQuestionnaire for the Self-assessment of University Service-learning Experiences (QaSLu)

Item text

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

InstrumentQuestionnaire for the Self-assessment of University Service-learning Experiences(QaSLU)
Mean words per item11.378
Mean characters per item83.911
Mean characters per response7.200
Flesch-Kincaid grade level13.547

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("QaSLU_Prado_2024")
# Python
pip install irw

import irw
df = irw.fetch("QaSLU_Prado_2024")

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