About this table

Description	7-item Likert scale (1-5) measuring current academic adaptation/satisfaction among first-year Spanish social-science university students (N=953).
Reference	Corti et al. (2023). Initial adaptation among university student: The case of the social sciences. PLOS ONE.
DOI	10.1371/journal.pone.0294440
Licence	CC BY 4.0
Source data	https://doi.org/10.1371/journal.pone.0294440

Size and shape

Responses	6,658
Respondents	953
Items	7
Response categories	5
Responses per respondent	6.986
Responses per item	951.143
Density	0.998
Longitudinal	FALSE

Classification

sample	Educational
measurement tool	Survey/questionnaire
item format	Likert Scale/selected response
primary language(s)	spa

Item text

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

Instrument	First Trimester Assessment Questionnaire (FTAQ)
Mean words per item	6.571
Mean characters per item	37.857
Mean characters per response	2
Flesch-Kincaid grade level	6.982

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, corti_2023_academic_adaptation.py. 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.

Column	What it is	Source column
IRW standard columns
`id`	IRW 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
`item`	IRW standard Item identifier.	made in the build script
`resp`	IRW standard Response value.	made in the build script
Respondent covariates
`cov_edad`	Covariate	`edad`
`cov_field_of_study`	Covariate `1` = ADE · `2` = Pedagogia · `3` = Ed. Infantil	`ensenyament`
`cov_sexo`	Covariate `1` = Mujer · `2` = Hombre	`sexo`

Covariate value labels are the source’s own, as the IRW recorded them. What item codes and any unlabelled values stand for is in the codebook released with the source data.

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant 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("corti_2023_academic_adaptation")

# Python
pip install irw

import irw
df = irw.fetch("corti_2023_academic_adaptation")

How to cite

@article{Corti_2023, title={Initial adaptation among university student: The case of the social sciences}, volume={18}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0294440}, DOI={10.1371/journal.pone.0294440}, number={11}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Corti, Franciele and Llanes, Juan and Dorio Alcaraz, Inmaculada and Freixa Niella, Montserrat}, editor={García-Iglesias, Juan Jesús}, year={2023}, month=Nov, pages={e0294440} }

Version and provenance

IRW version	v566
Redivis dataset	item_response_warehouse_5 v8.4
Redivis dataset DOI	10.57761/0gqp-zn75
Manifest pin for this IRW version	v8.4
Metadata source	irw_meta v43.0