amiot_2022_mlq_presence

12,069 responses from 2,423 respondents to 5 items.

One of 5 tables from the same source; this one is the mlq_presence table. The others are listed under Related tables.

About this table

DescriptionMeaning in Life Questionnaire, presence-of-meaning subscale: items 1, 4, 5, 6 and 9 of the published instrument on a 1-7 scale, 2,423 respondents, 12,069 responses. Item codes keep the published numbering rather than being renumbered 1-5, so they still point at the source item. From the OSF deposit's SPSS file, April 2020, Leger LEO probability-based Canadian panel. Raw item columns only: the deposit's reverse-scored copies (suffix _r) and its per-scale composite (the bare prefix column) are excluded. Missing responses are genuine and dropped, not imputed -- the paper describes no imputation.
ReferenceAmiot, C. E., Gagné, C., & Bastian, B. (2022). Pet ownership and psychological well-being during the COVID-19 pandemic. Scientific Reports, 12, 6091. https://doi.org/10.1038/s41598-022-10019-z
DOI10.1038/s41598-022-10019-z
LicenceCC BY 4.0
Source datahttps://osf.io/56sbh/

Size and shape

Responses12,069
Respondents2,423
Items5
Response categories7
Responses per respondent4.981
Responses per item2413.800
Density0.996
LongitudinalFALSE

Classification

sampleInternet-based
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng, fre

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, amiot_2022_pets.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.

ColumnWhat it isSource column
IRW standard columns
idIRW 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
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Respondent covariates
cov_age_bandCovariate
13 labelled values2 = 18 to 21 years old · 3 = 22 to 24 years old · 4 = 25 to 29 years old · 5 = 30 to 34 years old · 6 = 35 to 39 years old · 7 = 40 to 44 years old · 8 = 45 to 49 years old · 9 = 50 to 54 years old · 10 = 55 to 59 years old · 11 = 60 to 64 years old · 12 = 65 to 69 years old · 13 = 70 to 74 years old · 14 = 75 or older
age
cov_educationCovariate
9 labelled values1 = Primary school diploma · 2 = High school diploma · 3 = Diploma of Collegial studies (CÉGEP) · 4 = Professional studies diploma · 5 = Bachelor’s degree · 6 = Master’s degree · 7 = Doctoral degree · 96 = Other, please specify: · 98 = Refusal
EDUC
cov_genderCovariate 1 = Male · 2 = Female · 96 = Other, please specify:gender
cov_income_bandCovariate
12 labelled values1 = Less than $20,000 · 2 = $20, 000 to $39, 999 · 3 = $40, 000 to $59, 999 · 4 = $60, 000 to $79, 999 · 5 = $80, 000 to $99, 999 · 6 = $100, 000 to $119, 999 · 7 = $120, 000 to $139, 999 · 8 = $140, 000 to $159, 999 · 9 = $160, 000 to $179, 999 · 10 = $180, 000 to $199, 999 · 11 = Over $200, 000 · 99 = I prefer not to answer
INCOME
cov_pet_ownerCovariate 1 = Yes · 2 = NopetOwn
cov_survey_languageCovariate 1 = English / Anglais · 2 = Français / FrenchQ0

Covariate value labels are the source’s own, as the IRW recorded them. What item codes and any unlabelled values stand for is in the codebook released with the source data.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant 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("amiot_2022_mlq_presence")
# Python
pip install irw

import irw
df = irw.fetch("amiot_2022_mlq_presence")

How to cite

@article{Amiot_2022, title={Pet ownership and psychological well-being during the COVID-19 pandemic}, volume={12}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/s41598-022-10019-z}, DOI={10.1038/s41598-022-10019-z}, number={1}, journal={Scientific Reports}, publisher={Springer Science and Business Media LLC}, author={Amiot, Catherine E. and Gagné, Christophe and Bastian, Brock}, year={2022}, month=Apr }

Version and provenance

IRW versionv549
Redivis datasetitem_response_warehouse_6 v5.10
Redivis dataset DOI10.57761/8zbf-t397
Manifest pin for this IRW versionv5.10
Metadata sourceirw_meta v39.0