2,826 responses from 942 respondents to 3 items.
One of 4 tables from the same source; this one is the academic_procrastination table. The others are listed under Related tables.
| Description | 3-item academic-procrastination scale (1-5 Likert), same Chinese university EFL sample as hua_2023_efl_academic_self_concept, n=942 |
|---|---|
| Reference | Hua Y, et al (2023). The relationship between Chinese university students' learning preparation and learning achievement within the EFL blended teaching context in COVID-19 post-epidemic era: The mediating effect of learning methods. PLOS ONE. |
| DOI | 10.1371/journal.pone.0280919 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0280919 |
| Responses | 2,826 |
|---|---|
| Respondents | 942 |
| Items | 3 |
| Response categories | 5 |
| Responses per respondent | 3 |
| Responses per item | 942 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Educational |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, hua_2023_efl_learning_scales.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 |
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:
# R (no account needed)
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("hua_2023_efl_academic_procrastination")
# Python (needs a free Redivis account)
pip install irw
import irw
df = irw.fetch("hua_2023_efl_academic_procrastination")
@article{Hua_2023, title={The relationship between Chinese university students’ learning preparation and learning achievement within the EFL blended teaching context in COVID-19 post-epidemic era: The mediating effect of learning methods}, volume={18}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0280919}, DOI={10.1371/journal.pone.0280919}, number={1}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Hua, Meng and Wang, Lin}, editor={Buragohain, Dipima}, year={2023}, month=Jan, pages={e0280919} }
| IRW version | v540 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v13.0 |
| Redivis dataset DOI | 10.57761/w8zx-td92 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v37.0 |