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
RITA PAVSIC - Module Teorie e tecniche nella ricerca valutativa. La valutazione - SPS/07 - 3 CFU
RITA PAVSIC - Module Teorie e tecniche nella ricerca valutativa. Approcci multi criteri e approcci comparativi - SPS/07 - 3 CFU

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 paradoxes of multivariate 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)

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.

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