orsini_2018_vitality

5,544 responses from 924 respondents to 6 items.

One of 5 tables from the same source; this one measures Subjective vitality. The others are listed under Related tables.

About this table

DescriptionChilean dental students: subjective vitality scale, 6 items, 1-7; N = 924 (years 1-6, Universidad San Sebastián, 2016); cov_year_of_study, cov_gender, cov_age.
ReferenceOrsini, C. A., Binnie, V. I., & Tricio, J. A. (2018). Motivational profiles and their relationships with basic psychological needs, academic performance, study strategies, self-esteem, and vitality in dental students in Chile [Data set]. Harvard Dataverse, V1. https://doi.org/10.7910/DVN/SHNVHF
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/SHNVHF

Size and shape

Responses5,544
Respondents924
Items6
Response categories7
Responses per respondent6
Responses per item924
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)spa
construct nameSubjective vitality

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, orsini_2018_dental_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_ageCovariate age
cov_genderCovariate Gender
cov_year_of_studyCovariate Year of study

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("orsini_2018_vitality")
# Python
pip install irw

import irw
df = irw.fetch("orsini_2018_vitality")

How to cite

@misc{https://doi.org/10.7910/dvn/shnvhf,
  doi = {10.7910/DVN/SHNVHF},
  url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/SHNVHF},
  author = {A. Orsini, Cesar and I. Binnie, Vivian and A. Tricio, Jorge},
  keywords = {Medicine, Health and Life Sciences, : Chile; Dental student; Motivation; Personal satisfaction; Self concept},
  title = {Motivational profiles and their relationships with basic psychological needs, academic performance, study strategies, self-esteem, and vitality in dental students in Chile},
  publisher = {Harvard Dataverse},
  year = {2018}
}

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