bfi2_zhang_2025

79,888 responses from 1,335 respondents to 60 items.

About this table

DescriptionThe item-level data comes from 1,335 participants from UBC. Participants were randomly assigned to complete the BFI-2 in one of the four scale formats: the original Likert version or one of three alternative formats (Expanded, Item-Specific-Full, or Item-Specific-Light).
ReferenceZhang, X., Huang, M., Sun, J., & Savalei, V. (2025). Improving the measurement of the Big Five via alternative formats for the BFI-2. PubMed, 1–22. https://doi.org/10.1080/00223891.2025.2531187
DOI10.1080/00223891.2025.2531187
LicenceCC BY 4.0
Source datahttps://osf.io/d79c3

Size and shape

Responses79,888
Respondents1,335
Items60
Response categories5
Responses per respondent59.841
Responses per item1331.467
Density0.997
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational, Internet-based
construct typePersonality
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct nameThe Big Five Inventory-2 (BFI-2) and three Alternative Formats

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, bfi2_zhang_2025.R. 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
Other columns
itemcov_formatFrom source Covariates invariant to measurement probes (item-level attributes).self_condition

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("bfi2_zhang_2025")
# Python
pip install irw

import irw
df = irw.fetch("bfi2_zhang_2025")

How to cite

@article{Zhang_2025, title={Improving the Measurement of the Big Five via Alternative Formats for the BFI-2}, ISSN={1532-7752}, url={http://dx.doi.org/10.1080/00223891.2025.2531187}, DOI={10.1080/00223891.2025.2531187}, journal={Journal of Personality Assessment}, publisher={Informa UK Limited}, author={Zhang, Xijuan and Huang, Muhua and Sun, Jessie and Savalei, Victoria}, year={2025}, month=aug, pages={1–22} }

Version and provenance

IRW versionv549
Redivis datasetitem_response_warehouse_2 v33.0
Redivis dataset DOI10.57761/6m04-m698
Manifest pin for this IRW versionv33.0
Metadata sourceirw_meta v39.0