gilbert_meta_101

49,598 responses from 256 respondents to 9 items.

About this table

DescriptionParticipants rated 9 depression-related symptoms daily via digital self-report during treatment.
ReferenceSchumacher, L., Klein, J. P., Elsaesser, M., Härter, M., Hautzinger, M., Schramm, E., & Kriston, L. (2023). Implications of the network theory for the treatment of mental disorders: a secondary analysis of a randomized clinical trial. JAMA psychiatry, 80(11), 1160-1168.
DOI10.1001/jamapsychiatry.2023.2823
LicenceCC BY-NC-SA 4.0
Source datahttps://osf.io/fhqmk/

Size and shape

Responses49,598
Respondents256
Items9
Response categories2
Responses per respondent193.742
Responses per item5510.889
Density21.527
LongitudinalTRUE

Classification

age rangeAdult (18+)
sampleClinical
construct typeAffective/mental health
measurement toolSurvey/questionnaire
item formatSlider/continuous
primary language(s)eng
construct nameDepression Symptom Dynamics

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

InstrumentParticipants rated 9 depression-related symptoms daily via digital self-report during treatment.
Mean words per item2.444
Mean characters per item25.556
Mean characters per response25
Flesch-Kincaid grade level13.859

Columns

iditemresptreatwave

Get the data

Licence: CC BY-NC-SA 4.0 — non-commercial use only; adaptations must be shared under the same licence.

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python:

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("gilbert_meta_101")
# Python
pip install irw

import irw
df = irw.fetch("gilbert_meta_101")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse_2 v19.0
Redivis dataset DOI10.57761/qtx5-4m80
Manifest pin for this IRW versionv19.0
Metadata sourceirw_meta v23.0