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What influences the perception of immigration in Italian adolescents? An analysis with CUB models for rating data

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  • Ribecco, Nunziata
  • D'Uggento, Angela Maria
  • Labarile, Angela

Abstract

Surveys are the traditional means of measuring individuals’ opinions and perceptions through the expression of ratings on ordinal scales. CUB models, introduced by Piccolo in 2003 [1,2], are particularly useful for the analysis of such data, as they allow researchers to investigate the mechanism of selection and weighting of the two main latent components involved, uncertainty and feeling, with a mixture of a discrete Uniform and a shifted Binomial distributions, respectively. In order to understand how adolescents perceive the immigration phenomenon and the possibilities of social inclusion of migrants, a survey was conducted with about 1,200 students of some high schools participating in the Italian Ministry of Education Project for Scientific Degree in Statistics (PLS in Statistics) in Southern Italy. CUB regression models were used to investigate the main covariates that can explain different perceptions of the immigration phenomenon. Gender, Age, High school type, Geographic area of residence and Level of information emerged as the most important contextual factors, along with socio-demographic characteristics, influencing the development of critical thinking about the impact of immigration on the host society. In addition, the media and educators play a fundamental role in providing accurate information. Understanding the role of these factors could be useful in planning educational activities and thematic contents that address responsibility, the promotion of intercultural dialog, hospitality and cooperation. Educational institutions undoubtedly play a key role in building global citizenship among young people.

Suggested Citation

  • Ribecco, Nunziata & D'Uggento, Angela Maria & Labarile, Angela, 2022. "What influences the perception of immigration in Italian adolescents? An analysis with CUB models for rating data," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    • Handle: RePEc:eee:soceps:v:82:y:2022:i:pb:s0038012122000799
    DOI: 10.1016/j.seps.2022.101295
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    References listed on IDEAS

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    1. Maria Iannario & Domenico Piccolo, 2016. "A comprehensive framework of regression models for ordinal data," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 233-252, August.
    2. Stefania Capecchi & Rosaria Simone, 2019. "A Proposal for a Model-Based Composite Indicator: Experience on Perceived Discrimination in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 95-110, January.
    3. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    4. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2020. "Ordinal Data Models for No-Opinion Responses in Attitude Survey," Sociological Methods & Research, , vol. 49(1), pages 250-276, February.
    5. D'Elia, Angela & Piccolo, Domenico, 2005. "A mixture model for preferences data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 917-934, June.
    6. Domenico Piccolo & Rosaria Simone & Maria Iannario, 2019. "Cumulative and CUB Models for Rating Data: A Comparative Analysis," International Statistical Review, International Statistical Institute, vol. 87(2), pages 207-236, August.
    7. Domenico Piccolo & Rosaria Simone, 2019. "Rejoinder to the discussion of “The class of cub models: statistical foundations, inferential issues and empirical evidence”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 477-493, September.
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