bertani_2024_afccq

27,899 responses from 1,213 respondents to 23 items.

About this table

DescriptionAge-Friendly Cities and Communities Questionnaire, Italian version (AFCCQ-IT, 23 items, scored -2 to +2), 1,213 adults 65+ in Verona, Venice and Palermo interviewed by telephone in 2023.
ReferenceBertani, M., van Hoof, J., & Dikken, J. (2025). Getting older people's voices heard: A quantitative study using the validated Italian Age-Friendly Cities and Communities Questionnaire in Venice, Verona and Palermo. Journal of Cross-Cultural Gerontology, 40, 209-234. https://doi.org/10.1007/s10823-025-09532-1
DOI10.1007/s10823-025-09532-1
LicenceCC BY 4.0
Source datahttps://doi.org/10.5281/zenodo.11084903

Size and shape

Responses27,899
Respondents1,213
Items23
Response categories5
Responses per respondent23
Responses per item1,213
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleGeneral/non-specific
construct typeOpinion/attitude
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)ita
construct nameAge-friendliness of city (AFCCQ)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, bertani_2024_afccq.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_ageCovariate age
cov_born_italyCovariate 1 = Italia · 2 = otherd3
cov_cancerCovariate 1 = yes · 2 = nod10_5
cov_cityCovariate 23091 = Verona · 27042 = Venezia · 82053 = Palermocomune
cov_diabetesCovariate 1 = yes · 2 = nod10_1
cov_districtCovariate
23 labelled values1 = verona CENTRO STORICO · 2 = verona NORD-OVEST · 3 = verona OVEST · 4 = verona SUD-OVEST · 5 = verona SUD · 6 = verona EST · 7 = verona SUD-EST · 8 = verona NORD-EST · 11 = San Marco - Castello - Sant'Elena - Cannaregio , Dorsoduro - S. Polo - S. Croce - Giudecca - Sacca Fisola, Murano, Buran · 12 = Lido - Malamocco - Alberoni , Pellestrina - San Pietro in Volta (Lido-Pellestrina) · 13 = Favaro - Campalto (Favaro-Veneto) · 14 = Carpenedo - Bissuola (Mestre-Carpenedo) · 15 = Cipressina - Zelarino - Trivignano , Chirignago - Gazzera (Chirignago-Zelarino) · 16 = Marghera - Catene - Malcontenta (Marghera) · 21 = TRIBUNALI-CASTELLAMMARE - PALAZZO REALE - MONTE DI PIETA' (circoscrizione 1) · 22 = una parte di ORETO- STAZIONE - SETTECANNOLI - BRANCACCIO-CIACULLI (circoscrizione 2) · 23 = una parte di ORETO- STAZIONE - VILLAGRAZIA-FALSOMIELE (circoscrizione 3) · 24 = MONTEGRAPPA - S. ROSALIA - CUBA-CALATAFIMI - MEZZOMONREALE -VILLA TASCA - ALTARELLO - BOCCADIFALCO (circoscrizione 4) · 25 = ZISA - NOCE - UDITORE-PASSO DI RIGANO - BORGO NUOVO (circoscrizione 5) · 26 = CRUILLAS - S.GIOVANNI APOSTOLO - RESUTTANA - S.LORENZO (circoscrizione 6) · 27 = TOMMASO NATALE - SFERRACAVALLO - PARTANNA MONDELLO - PALLAVICINO - ARENELLA - VERGINE MARIA (circoscrizione 7) · 28 = MALASPINA -PALAGONIA - LIBERTA' - POLITEAMA - MONTEPELLEGRINO (circoscrizione 8) · 50 = ALTRO
d5
cov_educationCovariate 1 = Primary education, Lower secondary education · 2 = Upper secondary education, Post-secondary non-tertiary education · 3 = Short-cycle tertiary education, Bachelor or equivalent (Bachelor's degree) · 4 = Master's degree or equivalent (Master's degree), Doctoral degree or equivalent (Doctorate)d4
cov_genderCovariate 1 = Male · 2 = Femaled1
cov_heartCovariate 1 = yes · 2 = nod10_4
cov_help_at_homeCovariate 1 = yes · 2 = nod9
cov_help_familyCovariate 0 = not selected · 1 = yesd91_1
cov_help_friendCovariate 0 = not selected · 1 = yesd91_2
cov_help_municipalCovariate 0 = not selected · 1 = yesd91_5
cov_help_otherCovariate 0 = not selected · 1 = yesd91_6
cov_help_paidCovariate 0 = not selected · 1 = yesd91_3
cov_housingCovariate 1 = owner · 2 = public/social housing · 3 = rentd7
cov_kidneyCovariate 1 = yes · 2 = nod10_2
cov_life_satisfactionCovariate d12_1
cov_liverCovariate 1 = yes · 2 = nod10_6
cov_lives_aloneCovariate 1 = alone · 2 = with another persond8
cov_n_chronicCovariate D10
cov_respiratoryCovariate 1 = yes · 2 = nod10_3
cov_walking_aidCovariate 1 = yes · 2 = nod11
cov_years_in_cityCovariate d6

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("bertani_2024_afccq")
# Python
pip install irw

import irw
df = irw.fetch("bertani_2024_afccq")

How to cite

@article{Bertani_2025, title={Getting Older People’s Voices Heard: A Quantitative Study Using the Validated Italian Age-Friendly Cities and Communities Questionnaire in Venice, Verona and Palermo}, volume={40}, ISSN={1573-0719}, url={http://dx.doi.org/10.1007/s10823-025-09532-1}, DOI={10.1007/s10823-025-09532-1}, number={2}, journal={Journal of Cross-Cultural Gerontology}, publisher={Springer Science and Business Media LLC}, author={Bertani, Michele and van Hoof, Joost and Dikken, Jeroen}, year={2025}, month=May, pages={209–234} }

Version and provenance

IRW versionv584
Redivis datasetitem_response_warehouse_6 v5.11
Redivis dataset DOI10.57761/qahq-j855
Manifest pin for this IRW versionv5.11
Metadata sourceirw_meta v44.0