03383 - Social Statistics

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

• Online materials available on Virtuale.
• Piergiorgio Corbetta, Metodologia e tecniche della ricerca sociale. Il Mulino (chapters 1 to 8, including chapter 9 for SVIC students only)
• Matteo Mazziotta, Adriano Pareto, Gli indici sintetici. Giappichelli Editore (parts I and II)

Optional readings

• Matteo Mazziotta, Adriano Pareto (Eds.), Gli indici sintetici. Giappichelli Editore (part III)

Suggested readings

Gender statistics, gender data gap, data feminism

• Caroline Criado Pérez, Invisibili. Einaudi
• Catherine d'Ignazio & Lauren F. Klein, Data Feminism. MIT Press (https://data-feminism.mitpress.mit.edu)
• Catherine d'Ignazio, Counting Feminicide: Data Feminism in Action. MIT Press (https://mitpressonpubpub.mitpress.mit.edu/counting-feminicide)
• Chiara Lalli & Sonia Montegiove, Mai dati. Dati aperti (sulla 194). Perché sono nostri e perché ci servono per scegliere. Fandango Libri
• Emanuela Griglié & Guido Romeo, Per soli uomini. Il maschilismo dei dati, dalla ricerca scientifica al design. Codice edizioni

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

Data activism:

• Sarah Williams, Data Action: Using Data for Public Good. MIT Press (https://direct.mit.edu/books/book/4983/Data-ActionUsing-Data-for-Public-Good)

Teaching methods

The course is designed according to the Integrative Digital Teaching model proposed by the Innovation in teaching project of the University of Bologna. Teaching methods include both student participation in face-to-face lectures and self-paced activities based on a variety of teaching materials, such as slides, web resources, data sets, scholarly articles, infographics, reports, data visualizations, indicator dashboards, interactive web pages, data and metadata repositories, opinion polls, press articles, textual and video content for social networks, and exercises.

Assessment methods

Students will be evaluated through a written examination and (if need be) an oral examination.

Optionally, students can sign up for a mid-term written examination to be held at the end of the first lectures cycle.

Teaching tools

Power Point presentations, Microsoft Excel, Stata 17.

Office hours

See the website of Francesca Tosi