- Docente: Francesca Tosi
- Credits: 10
- SSD: SECS-S/05
- Language: Italian
- Teaching Mode: Traditional lectures
- Campus: Bologna
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Corso:
First cycle degree programme (L) in
International Development and Cooperation (cod. 8890)
Also valid for First cycle degree programme (L) in Statistical Sciences (cod. 8873)
Learning outcomes
After completing the course the student has knowledge of the main statistical sources, both national and international ones, as well as of the technical and methodological aspects of social research. In particular, the student is able to: - use the basic tools of quantitative analysis and verification of results in social research - knowingly exploit statistical sources
Course contents
The evolution of scientific thought in Social research - Quantitative vs. qualitative social research - The sources of Social Statistics - The gender data gap - Indicators and Composite Indices - Questionnaires, interviews, social research design - Psychological issues in understanding questions and answering - Techniques of administration of the questionnaire - Principles of sample design - Research in the field of hard to reach populations - Causality and experiments - Laboratory
Readings/Bibliography
Compulsory readings
For attending students:
- Online materials available on Virtuale.
- Piergiorgio Corbetta, Metodologia e tecniche della ricerca sociale. Il Mulino (chapters 1 to 9)
- Matteo Mazziotta, Adriano Pareto, Gli indici sintetici. Giappichelli Editore (parts I and II)
For non-attending students:
- Piergiorgio Corbetta, Metodologia e tecniche della ricerca sociale. Il Mulino (chapters 1 to 9)
- Matteo Mazziotta, Adriano Pareto (Eds.), Gli indici sintetici. Giappichelli Editore (parts I to III)
Suggested readings
Gender statistics, gender data gap, data feminism:
- Caroline Criado Pérez, Invisibili. Einaudi
- Catherine d'Ignazio e Lauren F. Klein, Data Feminism. MIT Press (https://data-feminism.mitpress.mit.edu)
Artificial intelligence and race bias/gender bias:
- Cathy O'Neil, Armi di distruzione matematica. Come i big data aumentano le disuguaglianze e minacciano la democrazia. Bompiani
- Safiya Umoja Noble, Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press
Teaching methods
Lectures, slides, websites and online materials, data sets, scientific papers, exercises both in class and at home
Assessment methods
Attending students will be evaluated through a paper to be prepared individually as a take-home exam, and through an oral examination.
Non-attending students will be evaluated through a written examination and (if need be) an oral examination.
There will be no mid-term examination.
Teaching tools
Power Point presentations, Microsoft Excel, Stata 17. Classes will be recorded and made available to the students via Panopto.
Office hours
See the website of Francesca Tosi
SDGs




This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.