54
STATISTICA
UNIVERSITÀ DEGLI STUDI DI NAPOLI "L'ORIENTALE"
Dati Generali
Periodo di attivitÃ
Syllabus
Obiettivi Formativi
General Objective: To provide an overview of the data sources, methodological frameworks, and the R statistical software necessary to approach the course topics from both theoretical and practical perspectives. Coursework will rely on official data sources, fostering a comprehensive understanding and an integrated view of the statistical tools discussed in class. Upon successful completion of the course, students will be able to critically evaluate official statistics and produce independent analyses based on real data.
Independent judgment: Students will be able to assess which methodological tools are most effective and appropriate for conducting statistical analyses aligned with specific research objectives.
Communication skills: Students will be able to interpret, discuss, and effectively communicate the results obtained from applying the learned methodologies.
Learning skills: Students will be able to independently conduct analyses using official data sources and specialized R packages relevant to the topics covered.
Prerequisiti
Familiarity with basic statistical concepts and general computer literacy are required.
Metodi didattici
The course is divided into theoretical lectures (approximately 22-24 hours), student seminars on specific topics (approximately 2-4 hours), and computer lab sessions (approximately 10-12 hours). The exact allocation of hours will depend on the number of students and the topics chosen by the participants. Because the course structure is highly cumulative (each topic builds upon previously covered material), active classroom participation and consistent home study are strongly recommended. Homework assignments will be assigned regularly strictly to monitor progress in using the R software; however, these will not be graded or count towards the final exam.
The course is conducted in English; however, it can be taught in Italian depending on the students' preference.
Verifica Apprendimento
For attending students, the exam consists of a written report analyzing official data, followed by a seminar-style presentation. The evaluation criteria include: the appropriateness of the tools used to address the research objective, proficiency in data analysis using R software, the ability to critically discuss the obtained results, and the accuracy of calculations. The final grade is scored out of thirty. Please note that the use of AI tools (e.g., ChatGPT) for writing the report or generating programming code is strictly prohibited.
For non-attending students, the exam consists of an oral presentation on syllabus topics to be agreed upon with me in advance.
Testi
Course materials and the reference bibliography will be provided in class at the beginning of the semester to ensure you receive the most up-to-date versions. Non-attending students may request these materials by contacting me via email.
Contenuti
The course is structured around three main topics: poverty, well-being, and social network analysis. The time allocated to each of these themes will be tailored to the interests of the class; therefore, their relative weight will be agreed upon with the students at the beginning of the semester. In any case, the topic of income, inequality and poverty) will occupy no less than two-thirds of the available time.
- INCOME. Definitions: real income, nominal income, Purchasing Power Parity (PPP). Equivalence scales: OECD, Carbonaro. Income distribution models: parametric and non-parametric modelling.
- INEQUALITY. Axioms. Indices of variability: Variance, logarithmic variance, mean logarithmic deviation, coefficient of variation, mean absolute difference. Concentration indices: the Gini coefficient. Graphical representation of concentration: the Lorenz Curve. Lorenz Dominance. Indices of entropy: the Theil index. Inequality indices: Interdecile index, income quintiles.
- POVERTY. The dimensions of poverty: absolute poverty, relative poverty, subjective poverty. The dynamics of poverty. Levels of poverty and needs. Poverty lines. Unidimensional poverty indices: Headcount ratio, Poverty gap index, Sen Index. Sen-Shorrocks-Thon index. Foster-Greer-Thorbecke index. Squared poverty gap index. Multidimensional poverty indices. Identification and aggregation stages. Human Poverty Index. Multidimensional Poverty Index.
- WELL-BEING. The concept of well-being. Measuring well-being. Indicators of well-being beyond GDP: Index of Sustainable Economic Welfare (ISEW), Genuine Progress Indicator (GPI). Multidimensional indicators of well-being: Inequality-adjusted Human Development Index (IHDI), Gender-related Development Index, Gross National Happiness (Bhutan), Subjective Well-Being Index. Measurement of well-being in Italy and in the world.
- SOCIAL NETWORK ANALYSIS. The development of Social Network Analysis. Simple and directed graphs. Definitions of node, edge, degree, density, path, diameter, geodesic distance, adjacency matrix, incidence matrix, component, core, clique. Local and global measures of centrality: degree, closeness, betweenness.
- THE R PROJECT FOR STATISTICAL COMPUTING. Introduction to the R language. Basic statistics and graphical representations. Income distribution analysis. Inequality measures. Poverty measures (ineq package). Social network analysis (sna package).
Lingua Insegnamento
Inglese
Altre informazioni
Prior to the start of the semester, students are required to contact me by email (esarno@unior.it) to enroll in the course. This facilitates the setup of the virtual classroom used for distributing course materials.