122,920 rated or chosen profiles from 3,073 respondents, in up to 20 tasks of 2 profiles varying on 10 attributes.
One of 2 tables from the same source; this one is the shoes table. The others are listed under Related tables.
| Description | 3,073 US MTurk respondents (December 2018) each rated 20 pairs of hypothetical athletic shoes described by 10 attributes including eco-friendly materials and gel cushioning, on a 7-point purchase-likelihood scale, under fully or partially randomized designs. |
|---|---|
| Reference | Horiuchi, Y., Markovich, Z., & Yamamoto, T. (2022). Does conjoint analysis mitigate social desirability bias? Political Analysis, 30(4), 535-549. |
| DOI | 10.1017/pan.2021.30 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/4WDVDB |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 3,073 |
|---|---|
| Rows (one per respondent, task and profile) | 122,920 |
| Tasks per respondent (most) | 20 |
| Profiles per task | 2 |
| Attributes | 10 |
| Topic | consumer products |
|---|---|
| A profile represents | product service |
| Country | US |
| Language shown | en |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): unchanged |
| Task order | recorded in the deposit |
| 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.
Outcomes:
| rating | How likely are you to purchase shoe 1? / How likely are you to purchase shoe 2? [rating, 1 (Very Unlikely) to 7 (Very Likely)] |
|---|
attr_ave_customer_reviewattr_best_sellerattr_brandattr_colorattr_eco_friendly_materialsattr_gel_cushioningattr_model_yearattr_priceattr_shippingattr_weightattrpos_ave_customer_reviewattrpos_best_sellerattrpos_brandattrpos_colorattrpos_eco_friendly_materialsattrpos_gel_cushioningattrpos_model_yearattrpos_priceattrpos_shippingattrpos_weightcov_age_groupcov_blockcov_env_1cov_env_2cov_env_3cov_env_4cov_env_5cov_party_idcov_racecov_sd_1cov_sd_2cov_sd_3cov_sd_4cov_sd_5cov_sd_6cov_sd_7cov_sd_8idprofileratingtasktrial_design
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("horiuchi_2022_sdb_shoes", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("horiuchi_2022_sdb_shoes", source="conj")
@article{Horiuchi_2021, title={Does Conjoint Analysis Mitigate Social Desirability Bias?}, volume={30}, ISSN={1476-4989}, url={http://dx.doi.org/10.1017/pan.2021.30}, DOI={10.1017/pan.2021.30}, number={4}, journal={Political Analysis}, publisher={Cambridge University Press (CUP)}, author={Horiuchi, Yusaku and Markovich, Zachary and Yamamoto, Teppei}, year={2021}, month=Sept, pages={535–549} }
| IRW version | v584 |
|---|---|
| Redivis dataset | irw_conjoint v4.2 |
| Redivis dataset DOI | 10.57761/qkye-q530 |
| Manifest pin for this IRW version | v4.2 |
| Metadata source | irw_meta v44.0 |