chen_2024_learning_motivation

2,952 responses from 328 respondents to 9 items.

One of 4 tables from the same source; this one measures Learning motivation (external and internal). The others are listed under Related tables.

About this table

DescriptionLearning motivation: external (M2-M5) and internal (M6-M10); 9 items, 1-5; 328 students (Chinese administration); covariates: cov_gender, cov_grade.
Referencechen, 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
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/TVUXDL

Notes

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Responses2,952
Respondents328
Items9
Response categories5
Responses per respondent9
Responses per item328
Density1
LongitudinalFALSE

Classification

sampleEducational
construct typeOpinion/attitude
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)chi
construct nameLearning motivation (external and internal)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, chen_2024_learning_motivation.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.

ColumnWhat it isSource column
IRW standard columns
idIRW 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
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Respondent covariates
cov_genderCovariate Gender
cov_gradeCovariate Grade

What item codes and covariate values stand for is in the codebook released with the source data.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant 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("chen_2024_learning_motivation")
# Python
pip install irw

import irw
df = irw.fetch("chen_2024_learning_motivation")

How to cite

@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}
}

Version and provenance

IRW versionv584
Redivis datasetitem_response_warehouse_5 v8.5
Redivis dataset DOI10.57761/41xy-5k56
Manifest pin for this IRW versionv8.5
Metadata sourceirw_meta v44.0