marcussonclavertz_2019_velten

8,454 responses from 106 respondents to 80 items.

About this table

Description	Velten Mood Induction Procedure statements (80 items, rated 1-very unhappy to 9-very happy), online validation, N=106
Reference	Marcusson-Clavertz D, Kjell ONE, Persson SD, Cardeña E (2019). Online validation of combined mood induction procedures. PLOS ONE, 14(1), e0217848. https://doi.org/10.1371/journal.pone.0217848
DOI	10.1371/journal.pone.0217848
Licence	CC BY 4.0
Source data	https://doi.org/10.1371/journal.pone.0217848.s002

Size and shape

Responses	8,454
Respondents	106
Items	80
Response categories	9
Responses per respondent	79.755
Responses per item	105.675
Density	0.997
Longitudinal	FALSE

Classification

age range	Adult (18+)
measurement tool	Survey/questionnaire
item format	Likert Scale/selected response
primary language(s)	eng

Item text

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

Instrument	Velten Mood Induction Procedure (80-statement pool)
Mean words per item	8.225
Mean characters per item	42.075
Mean characters per response	4
Flesch-Kincaid grade level	4.224

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, marcussonclavertz_2019_velten_mood_induction.py. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.

Column	What it is	Source column
IRW standard columns
`id`	IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).	made in the build script
`item`	IRW standard Item identifier.	made in the build script
`resp`	IRW standard Response value.	made in the build script
Respondent covariates
`cov_age`	Covariate	`age`
`cov_device`	Covariate	`device`
`cov_sex`	Covariate	`Gender`

What item codes and covariate values stand for is in the codebook released with the source data.

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("marcussonclavertz_2019_velten")

# Python
pip install irw

import irw
df = irw.fetch("marcussonclavertz_2019_velten")

How to cite

@article{Marcusson_Clavertz_2019, title={Online validation of combined mood induction procedures}, volume={14}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0217848}, DOI={10.1371/journal.pone.0217848}, number={6}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Marcusson-Clavertz, David and Kjell, Oscar N. E. and Persson, Stefan D. and Cardeña, Etzel}, editor={Olino, Thomas M.}, year={2019}, month=June, pages={e0217848} }

Version and provenance

IRW version	v569
Redivis dataset	item_response_warehouse_3 v14.0
Redivis dataset DOI	10.57761/rkkt-p907
Manifest pin for this IRW version	v13.0
Metadata source	irw_meta v43.0