mistry_2022_hardship

16,140 responses from 2,077 respondents to 8 items.

About this table

Description8-item COVID-19-era hardship/concern battery, Bangladeshi older adults, two repeated cross-sectional survey rounds
ReferenceMistry SK et al. (2022). Changes in loneliness prevalence and its associated factors among Bangladeshi older adults during the COVID-19 pandemic. PLOS ONE.
DOI10.1371/journal.pone.0277247
LicenceCC BY 4.0
Source datahttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0277247

Size and shape

Responses16,140
Respondents2,077
Items8
Response categories2
Responses per respondent7.771
Responses per item2017.500
Density0.971
LongitudinalFALSE

Classification

sampleProgram-based
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)ben

Item text

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

InstrumentCOVID-19 hardship and concern battery, ARCED Foundation older-adult telephone survey (items adapted from the Questionnaire for Assessing the Impact of the COVID-19 Pandemic on Older Adults, QAICPOA)
Mean words per item1
Mean characters per item2
Mean characters per response12.438
Flesch-Kincaid grade level-3.400

Columns

cov_age_groupcov_divisioncov_employmentcov_family_incomecov_family_sizecov_literatecov_living_alonecov_lonelycov_marital_statuscov_sexcov_survey_roundcov_urban_ruraliditemresp

Get the data

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("mistry_2022_hardship")
# Python
pip install irw

import irw
df = irw.fetch("mistry_2022_hardship")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse_4 v7.0
Redivis dataset DOI10.57761/cpj5-vm97
Manifest pin for this IRW versionv7.0
Metadata sourceirw_meta v23.0