3,608 responses from 328 respondents to 11 items.
One of 4 tables from the same source; this one measures Learning effectiveness (expertise, integrated skills). The others are listed under Related tables.
| Description | Learning effectiveness: expertise (G1-5) and integrated skills (G6-11); 11 items, 1-4; 328 students (Chinese administration); covariates: cov_gender, cov_grade. |
|---|---|
| Reference | chen, c. (2024). The study on the relationship between learning motivation and learning effectiveness: the mediating effect of learning engagement and the moderating effect of personality traits [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/TVUXDL |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/TVUXDL |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Responses | 3,608 |
|---|---|
| Respondents | 328 |
| Items | 11 |
| Response categories | 4 |
| Responses per respondent | 11 |
| Responses per item | 328 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Educational |
|---|---|
| construct type | Cognitive/educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
| construct name | Learning effectiveness (expertise, integrated skills) |
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_gender | Covariate | not traced |
cov_grade | 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("chen_2024_learning_effectiveness")
# Python
pip install irw
import irw
df = irw.fetch("chen_2024_learning_effectiveness")
@misc{https://doi.org/10.7910/dvn/tvuxdl,
doi = {10.7910/DVN/TVUXDL},
url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/TVUXDL},
author = {chen, cthomas},
keywords = {Social Sciences, learning motivation},
title = {The study on the relationship between learning motivation and learning effectiveness: the mediating effect of learning engagement and the moderating effect of personality traits},
publisher = {Harvard Dataverse},
year = {2024}
}
| 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 |