6,400 rated or chosen profiles from 200 respondents, in up to 16 tasks of 2 profiles varying on 4 attributes.

About this table

Description	Licensed psychotherapists in Germany (DocCheck panel, 2020) chose between pairs of blended-care application scenarios varying in recommendation source, effectiveness of the online component, online vs face-to-face time split and reimbursement; 200 respondents, 16 forced choices each (DCE).
Reference	Phillips, E. A., Himmler, S., & Schreyögg, J. (2022). Preferences of psychotherapists for blended care in Germany: A discrete choice experiment. BMC Psychiatry, 22, 112.
DOI	10.1186/s12888-022-03765-x
Licence	CC BY 4.0
Source data	https://osf.io/hy3ts/

Notes

Conjoint table (irw_conjoint). DCE. choice: 'Bitte wählen Sie eines der beiden Anwendungsszenarien für Blended Care, das Sie bevorzugen würden:' OPTION A / B, forced (no opt-out by design). Fixed blocked D-efficient design (trial_block; block 1 is the pre-change design seen by 37 respondents); German level text as displayed; free-text answers dropped; N = 200 as in the article.

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

Design

Topic	health covid
A profile represents	scenario
Country	DE
Language shown	de
Language of stored levels	de
Randomization restrictions	yes (stated by the source): fixed blocked Bayesian D-efficient design: 16 fixed choice sets per block (3 blocks, block 1 = pre-change design), so only the designed combinations occur; level shares unequal (effectiveness: 8 von 10 in 2,474 of 6,400 profile rows vs 1,921 and 2,005)
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:

choice	Bitte wählen Sie eines der beiden Anwendungsszenarien für Blended Care, das Sie bevorzugen würden: [choice, forced]

Columns

attr_effectivenessattr_recommendationattr_reimbursementattr_time_splitchoicecov_agecov_bc_conceivablecov_bc_formatcov_bc_relationshipcov_experience_ratingcov_financial_satisfactioncov_gendercov_likely_cbt_exercisescov_likely_chatbotscov_likely_diariescov_likely_gamescov_likely_psychoeducationcov_likely_videocov_professioncov_statecov_time_existing_clientscov_time_leisurecov_time_new_clientscov_time_trainingcov_uses_online_toolcov_workplaceidprofiletasktrial_block

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("phillips_2022_blended_care", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("phillips_2022_blended_care", source="conj")

How to cite

@article{Phillips_2022, title={Preferences of psychotherapists for blended care in Germany: a discrete choice experiment}, volume={22}, ISSN={1471-244X}, url={http://dx.doi.org/10.1186/s12888-022-03765-x}, DOI={10.1186/s12888-022-03765-x}, number={1}, journal={BMC Psychiatry}, publisher={Springer Science and Business Media LLC}, author={Phillips, Elena A. and Himmler, Sebastian and Schreyögg, Jonas}, year={2022}, month=Feb }

Respondents	200
Rows (one per respondent, task and profile)	6,400
Tasks per respondent (most)	16
Profiles per task	2
Attributes	4
Tasks with no profile chosen	0

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

phillips_2022_blended_care