2,030 rated or chosen profiles from 203 respondents, in up to 1 tasks of 10 profiles varying on 5 attributes.
| Description | Respondents in Durban ranked 10 fixed clothing-detergent profiles (durability, strength, price, form and size, scent) from most to least preferred. 203 respondents, 10 profiles in one task, rank stored in rating (1 = most preferred). |
|---|---|
| Reference | Kamwendo, A. (2021). Clothing detergent category data [Data set]. Mendeley Data. |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.17632/5bkgk7jzzs |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 203 |
|---|---|
| Rows (one per respondent, task and profile) | 2,030 |
| Tasks per respondent (most) | 1 |
| Profiles per task | 10 |
| Attributes | 5 |
| Topic | consumer products |
|---|---|
| A profile represents | product service |
| Country | ZA |
| Language shown | en |
| Language of stored levels | en |
| Randomization restrictions | unknown |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Outcomes:
| rating | Please identify the Clothing Detergent product category profiles presented in the poster. Rank the profiles in their order of preference, from most preferred (ranked 1st) to least preferred (ranked 10th). (Questionnaire 2, section C question 3; the sheet stores the rank per profile, inferred) [rating, 1 (most preferred (rank 1)) to 10 (least preferred (rank 10))] |
|---|
attr_durabilityattr_form_sizeattr_priceattr_scentattr_strengthcov_gender_codecov_generation_codeidprofileratingtask
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.
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("kamwendo_2021_detergent_ranking", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("kamwendo_2021_detergent_ranking", source="conj")
@misc{https://doi.org/10.17632/5bkgk7jzzs,
doi = {10.17632/5BKGK7JZZS},
url = {https://data.mendeley.com/datasets/5bkgk7jzzs},
author = {Kamwendo, Andrew},
keywords = {Consumer Behavior, Retail Buying},
title = {Clothing detergent category data},
publisher = {Mendeley},
year = {2021}
}
| 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 |