Analysis of young people not engaged in education, employment or training: a fuzzy MCA based approach

Authors

  • Anna Parola Department of Political Sciences, University of Naples Federico II, Naples, Italy
  • Alfonso Iodice D'Enza Department of Political Sciences, University of Naples Federico II, Naples, Italy

DOI:

https://doi.org/10.26398/IJAS.0033-003

Keywords:

NEETs, fuzzy multiple correspondence analysis, cluster analysis.

Abstract

Young adults in Neither Employment, Education or Training (NEET) are at high risk of adverse health outcome, in particular of mental health problems. The aim of this study is to identify the symptomatological profiles of young Italian NEETs. The consid- ered data set comes from the Adult Self Report (ASR) survey for assessing the mental health problems and it refer to a sample of 150’s Italian NEETs. A two-step unsupervised learning approach that involves fuzzy multiple correspondences analysis and clustering is applied to identify different symptomatological profiles of NEETs-related problems. The obtained results are compared to a principal component analysis-based approach. Finally, clinical implications in psychological practices are discussed.

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Published

2021-07-26

How to Cite

Parola, A., & Iodice D'Enza, A. (2021). Analysis of young people not engaged in education, employment or training: a fuzzy MCA based approach. Statistica Applicata - Italian Journal of Applied Statistics, 33(1), 65–82. https://doi.org/10.26398/IJAS.0033-003

Issue

No. 1 (2021): Vol. 33, Number 1

Section

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