934 responses from 467 respondents to 2 items.
One of 5 tables from the same source; this one measures Learning motivation: amotivation. The others are listed under Related tables.
| Description | Learning motivation, amotivation subscale, 2 items, 1-5; N = 467 Spanish university students of education degrees; cov_age, cov_gender, cov_degree, cov_year. |
|---|---|
| Reference | Galindo-Domínguez, H., Sainz-de-la-Maza, M., Campo, L., & Losada-Iglesias, D. (2025). The influence of learning motivation and procrastination on ChatGPT dependence. RIED-Revista Iberoamericana de Educación a Distancia. https://doi.org/10.5944/ried.45497 |
| DOI | 10.5944/ried.45497 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/WTMZID |
| Responses | 934 |
|---|---|
| Respondents | 467 |
| Items | 2 |
| Response categories | 5 |
| Responses per respondent | 2 |
| Responses per item | 467 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Educational, Targeted/specific |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | spa |
| construct name | Learning motivation: amotivation |
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.
| 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). | not traced |
item | IRW standard Item identifier. | not traced |
resp | IRW standard Response value. | not traced |
| Respondent covariates | ||
cov_age | Covariate | not traced |
cov_degree | Covariate | not traced |
cov_gender | Covariate | not traced |
cov_year | Covariate | not traced |
What item codes and covariate values stand for is in the codebook released with the source data.
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("galindo_2025_amotivation")
# Python
pip install irw
import irw
df = irw.fetch("galindo_2025_amotivation")
@article{Galindo_Dom_nguez_2025, title={The influence of learning motivation and procrastination on ChatGPT dependence}, ISSN={1138-2783}, url={http://dx.doi.org/10.5944/ried.45497}, DOI={10.5944/ried.45497}, journal={RIED-Revista Iberoamericana de Educación a Distancia}, publisher={UNED - Universidad Nacional de Educacion a Distancia}, author={Galindo-Domínguez, Héctor and Sainz-de-la-Maza, Martín and Campo, Lucía and Losada-Iglesias, Daniel}, year={2025}, month=Oct }
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_5 v8.5 |
| Redivis dataset DOI | 10.57761/41xy-5k56 |
| Manifest pin for this IRW version | v8.5 |
| Metadata source | irw_meta v44.0 |