STATISTICA MULTIVARIATA E RICERCA VALUTATIVA

Course Structure

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

  Lectures. Application of the contents learned to the resolution of empirical problems. Discussion of results.
  
  In-depth seminars on specific topics included in the program.

  Research activity: bibliographic consultation and data collection.
  
  Data analysis laboratories with training on statistical calculation packages.

  Paper presentations on the topics analyzed.

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.

  1. 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

  2. Multiple regression models - Nonlinear and logistics regression models - Log-linear models - Multilevel models - Structural equations models - Item Response Theory (IRT)

  3. Specialised topics on:

  • big data and data mining
  • agent-based models
• 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. Moreover, 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.

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.
  • 1. Fabbris L. (1997), Statistica multivariata. Analisi esplorativa dei dati, McGraw-Hill, Milano, pp. 3-77; 301-351 (analisi dei gruppi).

  • Gallucci M. e Leone l. (2012), Modelli statistici per le scienze sociali, Pearson, Milano, pp. 297-388 (analisi fattoriale).
  • 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.


  • 2. Bohrnstedt G. W. and Knoke D. (1998), Statistica per le scienze sociali, Il Mulino, Bologna, pp. 207-375.
  • Gallucci M. e Leone l. (2012), Modelli statistici per le scienze sociali, Pearson, Milano, pp. 41-64 (modelli di regressione multipla).
  • Hox J.J. (1995), Applied Multilevel Analysis, TT-Publikaties, Amsterdam, p. 1-30
  • Gallucci M. e Leone l. (2012), Modelli statistici per le scienze sociali, Pearson, Milano, pp. 389-422 (modelli di equazioni strutturali).
  • 3. 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.


  • Grow A., van Bavel J. (2017), Agent-Based Modelling in Population Studies: Concepts, Methods, and Applications, Berlin: Spinger, pp. 3-72.
• Theories and techniques in evaluation research. Multi criteria and comparative approaches

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

  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.

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

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