celik_2026_bpns

15,672 responses from 653 respondents to 24 items.

One of 6 tables from the same source; this one is the bpns table. The others are listed under Related tables.

About this table

DescriptionBasic Psychological Need Satisfaction and Frustration Scale (BPNSFS), 24 items on a 5-point scale, Turkish administration; 653 university students. From a deposit on personality traits and academic motivation as predictors of attitudes toward distance education; the same 653 respondents completed celik_2026_bfi and celik_2026_distance_ed_attitudes_16.
ReferenceCELIK, B. (2026). Personality Traits and Academic Motivation as Predictors of Attitudes Toward Distance Education. Mendeley Data, V1.
LicenceCC BY 4.0
Source datahttps://data.mendeley.com/datasets/hwp4wsb549

Size and shape

Responses15,672
Respondents653
Items24
Response categories5
Responses per respondent24
Responses per item653
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)tur

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, celik_2026_motivation_distance_ed.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 Yaş
cov_classCovariate Sınıf
cov_genderCovariate Cinsiyet
cov_schoolCovariate Okul

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:

# R (no account needed)
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("celik_2026_bpns")
# Python (needs a free Redivis account)
pip install irw

import irw
df = irw.fetch("celik_2026_bpns")

How to cite

@misc{https://doi.org/10.17632/hwp4wsb549,
  doi = {10.17632/HWP4WSB549},
  url = {https://data.mendeley.com/datasets/hwp4wsb549},
  author = {ÇELİK, Birol},
  keywords = {Motivation, Personality, Distance Education},
  title = {Personality Traits and Academic Motivation as Predictors of Attitudes Toward Distance Education},
  publisher = {Mendeley Data},
  year = {2026}
}

Version and provenance

IRW versionv540
Redivis datasetitem_response_warehouse_5 v8.0
Redivis dataset DOI10.57761/dkjz-6x68
Manifest pin for this IRW versionv8.0
Metadata sourceirw_meta v37.0