MODELLI E TECNICHE STATISTICHE PER L'ANALISI MULTIDIMENSIONALE DEI DATI

12 CFU - 1° and 2° Semester

Teaching Staff

VENERA TOMASELLI - Module MODELS AND STATISTICAL TECHNIQUES FOR THE ANALYSIS OF MULTI-DIMENSIONAL DATA - Factorial, clustering, big and mining data, network and neural techniques for statistical analysis. - SECS-S/05 - 3 CFU
VENERA TOMASELLI - Module MODELS AND STATISTICAL TECHNIQUES FOR THE ANALYSIS OF MULTI-DIMENSIONAL DATA - Models for the analysis of causal relations - SECS-S/05 - 3 CFU
FRANCESCO MAZZEO RINALDI - Module Theories and techniques in evaluation research. The evaluation - SPS/07 - 3 CFU
FRANCESCO MAZZEO RINALDI - Module Theories and techniques in evaluation research. Multi criteria and comparative approaches - SPS/07 - 3 CFU

Learning Objectives

• MODELS AND STATISTICAL TECHNIQUES FOR THE ANALYSIS OF MULTI-DIMENSIONAL DATA - Factorial, clustering, big and mining data, network and neural techniques for statistical analysis.
The student will acquire the theoretical and methodological knowledge for the understanding of: principles and the logic of multidimensional and multivariate statistical data analysis paradoxes of multivariate types of matrices techniques and for the use and interpretation of multidimensional techniques of data processing. The student will know technical procedures for the management of complex and large data-base through the use of specialized software for statistical data processing.
• MODELS AND STATISTICAL TECHNIQUES FOR THE ANALYSIS OF MULTI-DIMENSIONAL DATA - Models for the analysis of causal relations
Knowledge, use and interpretation of causal models. The student will know technical procedures for the management of complex and large data-base through the use of specialized software for statistical data processing.
• Theories and techniques in evaluation research. The evaluation
Ability to manage basic theoretical and operative tools with the purpose of shaping an evaluation research project, with a specific focus on both evaluation indicators and methodological issues
• Theories and techniques in evaluation research. Multi criteria and comparative approaches
To develop the capacity of choosing the research technique that is most suitable to the kind of evaluation to be done, strengthening, in particular, the ability of designing and managing a “focus group”

Detailed Course Content

• MODELS AND STATISTICAL TECHNIQUES FOR THE ANALYSIS OF MULTI-DIMENSIONAL DATA - Factorial, clustering, big and mining data, network and neural techniques for statistical analysis.

The principles and the logic of multidimensional and multivariate statistical data analysis

The types of matrices

Factor Analysis: principal factors and principal components

Multidimensional Scaling

Correspondence analysis: simple and multiple

Cluster analysis

Methods of fuzzy clustering

Specialised topics on:

• big data and data mining
• testual analysis
• network analysis
• neural networks
• MODELS AND STATISTICAL TECHNIQUES FOR THE ANALYSIS OF MULTI-DIMENSIONAL DATA - Models for the analysis of causal relations

Multiple regression models Nonlinear and logistics regression models Log-linear models Specialised topics on: • multilevel models • structural equations models • Item Response Theory (IRT)

• Theories and techniques in evaluation research. The evaluation

The module faces in key critical relationships the link between monitoring and evaluation, observing, in particular, the links between monitoring and evaluation indicators. Students will have the opportunity to identify key methodological issues to be considered in implementing monitoring systems effectively oriented to evaluation.

• Theories and techniques in evaluation research. Multi criteria and comparative approaches

The main objective of the module is to provide the student with the fundamentals of evaluation logic, with particular reference to: the basic elements that characterize the evaluation process, the main evaluation theories; and the impact evaluation approaches, addressing the main methodological issues.

Textbook Information

• MODELS AND STATISTICAL TECHNIQUES FOR THE ANALYSIS OF MULTI-DIMENSIONAL DATA - Factorial, clustering, big and mining data, network and neural techniques for statistical analysis.

Fabbris L. (1997), Statistica multivariata. Analisi esplorativa dei dati, McGraw-Hill, Milano, pp. 3-77; 163-295; 301-351.

Kosko B. (1995), Il fuzzy-pensiero. Teoria ed applicazioni della logica fuzzy, Baldini & Castaldi, Milano, pp. 13-57; 147- 183.

Sangalli A. (2000), L’importanza di essere fuzzy, Bollati Boringhieri, Torino, p. 19-147.

Rezzani A. (2013), Big Data, Apogeo Education, Maggioli editore, Santarcangelo di Romagna (RN).

Azzalini A., Scarpa B. (2004), Analisi dei dati e data mining, Springer, Berlin.

Fraire M., Rizzi A. (2011), Analisi dei dati per il data mining, Carocci, Roma.

Tuzzi A. (2003), L’analisi del contenuto, Carocci, Roma

D. F. Iezzi (2009), Statistica per le Scienze Sociali, Carocci, Roma (Cap. 13).

D. F. Iezzi (2009), Statistica per le Scienze Sociali, Carocci, Roma (Cap. 14).

Meraviglia C. (2001), Le reti neurali nella ricerca sociale, FrancoAngeli, Milano, pp. 13-78.

• MODELS AND STATISTICAL TECHNIQUES FOR THE ANALYSIS OF MULTI-DIMENSIONAL DATA - Models for the analysis of causal relations

Bohrnstedt G. W. and Knoke D. (1998), Statistica per le scienze sociali, Il Mulino, Bologna, pp. 207-375.

Hox J.J. (1995), Applied Multilevel Analysis, TT-Publikaties, Amsterdam, p. 1-30
Corbetta P. (2002), Metodi di analisi multivariata per le scienze sociali. I modelli di equazioni
strutturali, Il Mulino, Bologna, pp. 39-94.
Giampaglia G. (2008), Il modello di Rasch nella ricerca sociale, Liguori, Napoli.

• Theories and techniques in evaluation research. The evaluation

Bezzi, C., Cannavò L., Palumbo M. (2010) Costruire indicatori nella Ricerca Sociale e nella Valutazione, Milano, FrancoAngeli: pp. 19-56.

Mazzeo Rinaldi F., (2012) Il monitoraggio per la valutazione, Milano, FrancoAngeli: pp 17-43 pp 67-115.

• Theories and techniques in evaluation research. Multi criteria and comparative approaches

Stame N., (2016) Valutazione pluralista. Milano, Franco Angeli, pp 23-111.

Stern E. (2016) La valutazione di impatto. Una guida per committenti e manager preparata per Bond. Milano, Franco Angeli, pp 13-65.

Stame N. - a cura - (2007) Classici della valutazione. Milano, Franco Angeli, pp. 337-416.

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