STATISTICA MULTIVARIATA E RICERCA VALUTATIVA

Module TECNICHE, MODELLI E PROCEDURE DI CALCOLO PER L'ANALISI STATISTICA DEI DATI

Acquisition of knowledge and development of skills for multivariate data 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.

TECHNIQUES and STATISTICAL MODELS FOR MULTIDIMENSIONAL DATA ANALYSIS: Knowledge and expertise in single- and bi-variate statistical data analysis, principles of probability, parameter estimation and parametric and non-parametric tests for statistical inference.

Strongly recommended for empirical and laboratory calculation applications proposed in class and to be able to access the tests in progress.

1. Factorial analysis - Cluster analysis - Matching for risk analysis
2. Multiple Regression Models - Log-Linear Models - Non-linear and Logistic Regression Models - Multilevel Models - Structural Equation Models -
3. In-depth topics: software R Studio

             

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 Matching techniques: - https://openknowledge.worldbank.org/bitstream/handle/10986/25030/9781464807794.pdf?sequence=2&isAllowed=y

                                             -   https://www.amazon.it/Effect-Introduction-Research-Design-Causality/dp/1032125780

for software applications:
Hahs-Vaughn, D. L. (2017). Applied Multivariate Statistical Concepts. New York, NY: Routledge, pp. 1-56; 335-440
Digital manuals of the software used.

in Italian to consult, if necessary:
Gallucci M., Leone L., Berlingeri M. (2017), Modelli statistici per le scienze sociali, Pearson, Milano, pp. 323-406 (analisi fattoriale).

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

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

for software applications:
Hahs-Vaughn, D. L. (2017). Applied Multivariate Statistical Concepts. New York, NY: Routledge, pp. 57-272; 441-570.
Digital manuals of the software used.

in Italian to consult if necessary:
Bohrnstedt G. W. and Knoke D. (1998), Statistica per le scienze sociali, Il Mulino, Bologna, pp. 207-375 (non-linear regression models and logistics).
Gallucci M., Leone L., Berlingeri M. (2017), Modelli statistici per le scienze sociali, Pearson, Milano, pp. 41-98 (multiple regression models).

3. In-depth topics:

James Lang & Paul Teetor, R Cookbook, 2nd Edition (https://www.tidytextmining.com/)

 http://www.sthda.com/english/ https://app.rawgraphs.io/ 

  

 

TECHNIQUES and STATISTICAL MODELS FOR MULTIDIMENSIONAL DATA ANALYSIS: Project on data analysis. The written test is mandatory and includes open-ended questions. The written test is considered passed if the student receives a total mark of not less than 18/30 for the written test and a mark equal to 26/30 maximum will be recorded. In order to obtain a total mark higher than 26/30 it is necessary to take the oral exam.