7,406 rated or chosen profiles from 529 respondents, in up to 7 tasks of 2 profiles varying on 2 attributes.
| Description | German-speaking students (SoSci Survey, 2016, n = 529) made 7 choices between two Facebook-use situations varying place of use (at home / elsewhere) and motive (information seeking, companionship, escapism, entertainment); fixed design in 4 blocks of 7. |
|---|---|
| Reference | Pelzer, E. (2019). The potential of conjoint analysis for communication research. Communication Research Reports, 36(2), 136-147. |
| DOI | 10.1080/08824096.2018.1559138 |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/dbg7s/ |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 529 |
|---|---|
| Rows (one per respondent, task and profile) | 7,406 |
| Tasks per respondent (most) | 7 |
| Profiles per task | 2 |
| Attributes | 2 |
| Tasks with no profile chosen | 0 |
| Topic | technology ai privacy |
|---|---|
| A profile represents | scenario |
| Country | unknown |
| Language shown | de |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): fixed blocked design: 28 fixed pairs in 4 blocks of 7, one block per respondent; some pairs share a level; across the 56 design profiles each motive appears 14 times and each place 28 times (balanced) |
| 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:
| choice | Choice between two Facebook-use situations (paraphrase) [choice, forced] |
|---|
attr_motiveattr_placechoicecov_agecov_education_codecov_facebook_frequency_codecov_gender_codeidprofiletasktrial_situation
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("pelzer_2019_facebook_use", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("pelzer_2019_facebook_use", source="conj")
@article{Pelzer_2018, title={The Potential of Conjoint Analysis for Communication Research}, volume={36}, ISSN={1746-4099}, url={http://dx.doi.org/10.1080/08824096.2018.1559138}, DOI={10.1080/08824096.2018.1559138}, number={2}, journal={Communication Research Reports}, publisher={Informa UK Limited}, author={Pelzer, Elena}, year={2018}, month=Dec, pages={136–147} }
| 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 |