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 empirical research issues. Discussion of results.

  
  Seminars on specific topics included in the course.

  Research activity: literature research and data collection.
  
  Data analysis laboratories with training on statistical software.

  Paper presentations on the topics of the course.

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. Cluster analysis - Multidimensional Scaling - Correspondence analysis: simple and multiple Factor Analysis: principal factors and principal components -

  2. Multiple regression models - Nonlinear and logistics regression models - Structural equations models - Multilevel models -

   

  Specialised topics on:

  • 1. big data and data mining
  • 2. 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. Bartholomew D. J., Steele F., Moustaki I., Galbraith J. I. (2008). Analysis of Multivariate Social Science Data. Boca Raton, FL: CRC Press, Taylor & Francis, pp. 1-144; 175-208.

  for the software training:

  • Hahs-Vaughn, D. L. (2017). Applied Multivariate Statistical Concepts. New York, NY: Routledge, pp. 1-56; 335-440

   

  • 2. Bartholomew D. J., Steele F., Moustaki I., Galbraith J. I. (2008). Analysis of Multivariate Social Science Data. Boca Raton, FL: CRC Press, Taylor & Francis, pp. 145-174; 289-362.

  for the software training:

  • Hahs-Vaughn, D. L. (2017). Applied Multivariate Statistical Concepts. New York, NY: Routledge, pp. 57-272; 441-570.

   

  Specialised topics on:

  • 1. Foster I., Ghani R., Jarmin R. S., Kreuter F., Lane J. (2017). Big Data and Social Science. Boca Raton, FL: CRC Press, Taylor & Francis, pp. 1-240.
  • 2. Grow A., van Bavel J. (2017), Agent-Based Modelling in Population Studies: Concepts, Methods, and Applications, Berlin: Springer, 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.