fraijo_2022_mslq

28,107 responses from 347 respondents to 81 items.

About this table

DescriptionAcademic motivation and learning strategies: Dataset in a public university sample. IRW table fraijo_2022_mslq: 347 respondents x 81 items, 28107 responses.
ReferenceFraijo, J.; Norzagaray, C. (2022). Academic motivation and learning strategies: Dataset in a public university sample. Mendeley Data, V2.
LicenceCC BY 4.0
Source datahttps://data.mendeley.com/datasets/bsxgbt8wnp

Size and shape

Responses28,107
Respondents347
Items81
Response categories7
Responses per respondent81
Responses per item347
Density1
LongitudinalFALSE

Classification

age rangeMixed
child age (for child-focused studies)Adolescent (12-18y)
sampleEducational
measurement toolSurvey/questionnaire
primary language(s)spa

Item text

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

InstrumentCuestionario de Motivación y Estrategias de Aprendizaje (CEMEA; Motivated Strategies for Learning Questionnaire, MSLQ), 81 items
Mean words per item1
Mean characters per item2
Mean characters per response6.571
Flesch-Kincaid grade level-3.400

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, fraijo_2022_mslq.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_ageCovariate Age
cov_genderCovariate Gender
cov_residenceCovariate Student’s Residence
cov_shiftCovariate Shift
cov_university_yearCovariate University year

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("fraijo_2022_mslq")
# Python
pip install irw

import irw
df = irw.fetch("fraijo_2022_mslq")

How to cite

@misc{https://doi.org/10.17632/bsxgbt8wnp,
  doi = {10.17632/BSXGBT8WNP},
  url = {https://data.mendeley.com/datasets/bsxgbt8wnp},
  author = {{José Fraijo}},
  title = {Academic motivation and learning strategies: Dataset in a public university sample},
  publisher = {Mendeley},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

Version and provenance

IRW versionv549
Redivis datasetitem_response_warehouse_5 v8.1
Redivis dataset DOI10.57761/07zt-n365
Manifest pin for this IRW versionv8.1
Metadata sourceirw_meta v39.0