1,648 rated or chosen profiles from 103 respondents, in up to 16 tasks of 1 profile varying on 4 attributes.

About this table

Description	Indonesian millennials rated 16 fixed children's-clothing cards (educational print theme, size, colour, purchase channel; text in Indonesian/English theme names). 103 respondents, 16 single-profile cards, one 1-5 score.
Reference	Pertiwi, P. A. M., & Murniati, M. (2025). Industrial engineering approach to analyzing millennials' purchase preferences for sustainable educational fashion in business using conjoint analysis data [Data set]. Mendeley Data.
Licence	CC BY 4.0
Source data	https://doi.org/10.17632/8bj7b68ddm

Notes

Conjoint table (irw_conjoint_2). Single-profile rating conjoint: each respondent scored 16 fixed children's-clothing cards (theme, size, colour, purchase channel) on a 1-5 scale stored raw; the deposit gives no question wording or scale labels. Fixed 16-card subset of the 48 combinations (M appears on 8 cards, L and XL on 4 each); task = card number in column order (display order not documented). Covariates are two Indonesian-text purchase-habit answers.

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

Design

Topic	consumer products
A profile represents	product service
Country	ID
Language shown	id
Language of stored levels	id
Randomization restrictions	observed in the data (not documented by the source): fixed 16-card plan, a subset of the 4 x 3 x 2 x 2 = 48 combinations; size M is on 8 cards and L, XL on 4 each, so combinations are not independent and levels are unequally frequent
Task order	inferred from row order
Profile position	recorded in the deposit

Because the attribute levels were not randomized independently, an estimator has to account for the restriction; a plain difference in means across levels can mislead. Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.

Outcomes:

rating	unknown (no question wording in the deposit); score of each stimulus card, 1-5 [rating, 1 (unknown) to 5 (unknown)]

Columns

attr_colourattr_purchase_channelattr_sizeattr_themecov_purchase_frequencycov_purchase_placeidprofileratingtask

The layout (one row per respondent, task and profile; choice/rating outcomes; attr_ columns holding each attribute level as displayed) is defined in the IRW conjoint standard. What the outcomes asked is under Design above.

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant 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("pertiwi_2025_educational_fashion", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("pertiwi_2025_educational_fashion", source="conj")

How to cite

@misc{https://doi.org/10.17632/8bj7b68ddm,
  doi = {10.17632/8BJ7B68DDM},
  url = {https://data.mendeley.com/datasets/8bj7b68ddm},
  author = {Maharani Pertiwi, Putri Athar and Murniati, Murniati},
  keywords = {Questionnaire},
  title = {Industrial Engineering Approach to Analyzing Millennials’ Purchase Preferences for Sustainable Educational Fashion in Business Using Conjoint Analysis},
  publisher = {Mendeley Data},
  year = {2025}
}

Respondents	103
Rows (one per respondent, task and profile)	1,648
Tasks per respondent (most)	16
Profiles per task	1
Attributes	4

IRW version	v584
Redivis dataset	irw_conjoint_2 v1.4
Redivis dataset DOI	10.57761/hk97-wv30
Manifest pin for this IRW version	v1.4
Metadata source	irw_meta v44.0

pertiwi_2025_educational_fashion