1,402 rated or chosen profiles from 1,402 respondents, in up to 1 tasks of 1 profile varying on 2 attributes.
| Description | German online respondents each listened to one audio recording of a fictional public service encounter in which the employee's administrative language (control, informational, relational, or both) and the service decision (negative or positive) were randomized, then rated satisfaction, comprehensibility, reification, emotionality and complaisance on 10-point scales. 1,402 respondents. |
|---|---|
| Reference | Eckhard, S., & Friedrich, L. (2022). Linguistic features of public service encounters: How spoken administrative language affects citizen satisfaction. Journal of Public Administration Research and Theory, 34(1), 122-135. |
| DOI | 10.1093/jopart/muac052 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/OXWTNE |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 1,402 |
|---|---|
| Rows (one per respondent, task and profile) | 1,402 |
| Tasks per respondent (most) | 1 |
| Profiles per task | 1 |
| Attributes | 2 |
| Topic | public administration |
|---|---|
| A profile represents | scenario |
| Country | DE |
| Language shown | de |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): The language factor is four fixed bundles of seven linguistic features (codebook): features vary only together, as a 2 x 2 of informational x relational language; language and service decision are fully crossed (8 recordings, 150-204 respondents each). |
| Task order | inferred from row order |
| Profile position | inferred from row order |
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 | All in all, how satisfied are you with the bureaucratic encounter? (translated) [rating, 1 (not satisfied at all) to 10 (very satisfied)] |
|---|---|
| rating_complaisance | How committed was the public employee to your case? (translated) [rating, 1 (not at all) to 10 (Very much)] |
| rating_comprehensibility | How well were you able to follow the public employee's utterances? (translated) [rating, 1 (not good at all) to 10 (Very good)] |
| rating_emotionality | How important do you think your personal concerns and problems were to the public employee? (translated) [rating, 1 (not important at all) to 10 (Very important)] |
| rating_reification | How much understanding do you have of how the public employee behaved and the decisions she made? (translated) [rating, 1 (no understanding at all) to 10 (Full understanding)] |
attr_languageattr_service_decisioncov_age_groupcov_attention_passcov_benefitscov_educationcov_employmentcov_gendercov_last_encounter_satisfactioncov_months_since_contactcov_trust_armed_forcescov_trust_bundestagcov_trust_constitutional_courtcov_trust_federal_administrationcov_trust_federal_governmentcov_trust_justicecov_trust_local_administrationcov_trust_partiescov_trust_policecov_trust_state_administrationcov_trust_state_governmentcov_vote_intentionidprofileratingrating_complaisancerating_comprehensibilityrating_emotionalityrating_reificationtasktrial_imc_authoritytrial_listened_complete
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("eckhard_2022_administrative_language", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("eckhard_2022_administrative_language", source="conj")
@article{Eckhard_2022, title={Linguistic Features of Public Service Encounters: How Spoken Administrative Language Affects Citizen Satisfaction}, volume={34}, ISSN={1477-9803}, url={http://dx.doi.org/10.1093/jopart/muac052}, DOI={10.1093/jopart/muac052}, number={1}, journal={Journal of Public Administration Research and Theory}, publisher={Oxford University Press (OUP)}, author={Eckhard, Steffen and Friedrich, Laurin}, year={2022}, month=Dec, pages={122–135} }
| 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 |