- Data, Statistics and AI for Well-Being of People and Organisations
- A cura di Luigi Fabbris, Alberto Franci, Pietro Renzi
CUB models for ordinal data analysis and quality assessment
- Alessandro La Rocca
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1062-1.42
This study presents the application of CUB models (Combination of Uniform and shifted Binomial distributions) to ordinal data, with a focus on data quality assessment in survey research. CUB models account for two latent dimensions in respondent behavior: feeling (intentional evaluation) and uncertainty (randomness or confusion). Extensions like the CUBE model and covariate inclusion improve adaptability to real data, capturing effects such as overdispersion and scale misuse. Using data from the ISTAT survey "Families, Social Subjects, and Life Cycles", the analysis evaluates how well respondents engaged with sensitive attitudinal questions. The parameters π and δ serve as indicators of data reliability: low π values suggest high uncertainty, while high δ values point to response anomalies. Results show that CUB models can identify problematic items and enhance the reliability of ordinal measurements, making them valuable tools for both statistical modeling and survey quality diagnostics.
- Keywords:
- CUB models,
- Quality assessment,
- Latent variables,
- Rating scales,
- mixture models,
ISTAT, Italian National Institute of Statistics, Italy
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Informazioni sul capitolo
Titolo del capitolo
CUB models for ordinal data analysis and quality assessment
Autori
Alessandro La Rocca
Lingua
Inglese
DOI
10.36253/979-12-215-1062-1.42
Opera sottoposta a peer review
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
Data, Statistics and AI for Well-Being of People and Organisations
Curatori
Luigi Fabbris, Alberto Franci, Pietro Renzi
Opera sottoposta a peer review
Numero di pagine
420
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-1062-1
ISBN Print
979-12-215-1061-4
eISBN (pdf)
979-12-215-1062-1
eISBN (xml)
979-12-215-1063-8
Collana
Applied Statistics Series