# STATISTICA SOCIALE ED INFORMATIZZAZIONE DEI DATI

9 CFU - 1° Semester

### Teaching Staff

ROSARIO GIUSEPPE D'AGATA - Module INDICATOR SOURCES. TERRITORIAL DATA ANALYSIS. - SECS-S/05 - 3 CFU
ROSARIO GIUSEPPE D'AGATA - Module SAMPLING DESIGN. NON PARAMETRIC TESTS. - SECS-S/05 - 3 CFU
GIOVANNI GIUFFRIDA - Module STRUMENTI PER LA GESTIONE DI BASI DI DATI - INF/01 - 3 CFU

## Learning Objectives

• INDICATOR SOURCES. TERRITORIAL DATA ANALYSIS.
The first form aims to provide methodological tools directed to territorial data analysis. So, It focuses on typology and quality of data sources. Specifically the form highlights the role of indicators in the social research and provides statistical instruments for the construction of a composite indicator.
• SAMPLING DESIGN. NON PARAMETRIC TESTS.
The form is about theoretical and practical issues of statistical sampling. We will argue about random sampling techniques, touching on non-random sampling methods. In the second section of the form, non-parametric statistic test will be introduced. Namely, the section focus on one sample tests, two dependent samples tests and two independent samples test.
• STRUMENTI PER LA GESTIONE DI BASI DI DATI
Main goal is to provide knowledge on techniques and tools for data archive and management. Furthermore, we want to introduce analysis techniques for large data bases.

## Detailed Course Content

• INDICATOR SOURCES. TERRITORIAL DATA ANALYSIS.

Statistic sources; institutional statistics; data-base linkage; data transformation; index and rates; indicators, composite indicators.

• SAMPLING DESIGN. NON PARAMETRIC TESTS.

Sampling design, sampling size; sampling errors; Sampling selection criteria; non-parametric test for one-sample: Binomial test, χ2 test, Kolmogorov-Smirnov test. Two dependent samples test: McNemar test. Two independent samples test: Fisher, χ2, Median, Wilcoxon-Mann-Whitney.

• STRUMENTI PER LA GESTIONE DI BASI DI DATI
Main aim is to introduce tools and systems for data base management. We cover both theoretical and practical aspects. Given the huge amount of social data available today it is mandatory for the social science students to learn how to master management of such data. Interesting social phenomena can be derived by properly analyze such large data bases. Applications such as Facebook, online news interaction, email exchange, etc. give rise to very deep and interesting studies to understand social models. Such a large data base cannot be easily analyzed by using conventional data management techniques.

## Textbook Information

• INDICATOR SOURCES. TERRITORIAL DATA ANALYSIS.

STAT (2011), Navigando tra le fonti demografiche e sociali, ISTAT, Roma,
http://www3.istat.it/dati/catalogo/20100325_01/Navigando_tra_le_fonti_demografiche_sociali.pdf
Bonarini F. (2006), Guida alle fonti statistiche socio-demografiche, CLEUP, Padova, pp. 1-142; 231-308.
Cavaleri P. e Venturini F. (a cura di) (2004), Documenti e dati pubblici sul web. Guida all'informazione di fonte pubblica in rete, Il Mulino, Bologna.
D. F. Iezzi (2009), Statistica per le Scienze Sociali, Carocci, Roma (Cap. 10 e 11).
OECD (2008), Handbook on Constructing Composite Indicators. Methodology and user guide.,
www.oecd.org/publishing.

• SAMPLING DESIGN. NON PARAMETRIC TESTS.

G. Cicchitelli, A. Herzel e G. E. Montanari (1992), Il campionamento statistico, Il Mulino, Bologna, cap. III (§§ 1, 2, 3, 4, 5, 6, 7), pp. 69-84.
L. Fabbris (1989), L’indagine campionaria, N.I.S., Roma, capp. I (§ 1.4, 1.5, 1.6), pp. 24-39.
S. Siegel e N. J. Castellan jr. (1992), Statistica non parametrica, McGraw-Hill, Milano, cap. III
(§§ 3.4, 3.4.1, 3.4.2) e cap. IV (§§ 4.1, 4.2, 4.3), pp. 67-92; cap. V (§§ 5.1,5.2), pp. 113- 130; cap. VI (§§ 6.1, 6.2, 6.3, 6.4), pp. 151-191

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