Methodologies for the Social Sciences II: Quantitative (advanced course)

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Compulsory for the 1st year students of the PhD Programme in "Political Science and Sociology"

Compulsory for the 1st year students of the PhD Programme in "Transnational Governance"

Optional for the 2nd, 3rd and 4th year students of the PhD Programmes in "Political Science and Sociology" and in "Transnational Governance"

Optional for the 4th and 5th year students of the MA Programme in "Political and Social Sciences"


Compulsory - if not attending the course 'Methodologies for the Social Sciences II: quantitative'

The course covers some of the most popular methodologies for the quantitative analysis of social and political phenomena using observational data. More specifically, it consists in short lectures followed by laboratory sessions using the software STATA, one of the most powerful statistical software for quantitative data analysis.
The course places particular emphasis on the empirical work related to the statistical elaboration of the data used by the MA/PhD students in their master or doctoral research projects. Otherwise, other popular datasets are used to work on and apply widely used statistical techniques.

The course offers participants an applied perspectives on topics that will be previously defined by students’ research interests. Broadly speaking, it is focused on the analysis of the relationships between independent and dependent variables, the presentation of the results (also graphically) and their interpretation. The course will cover regression analysis, moving beyond ordinary least squares regression, and will explore other techniques such as time-series regression and survival analyses.

Students are strongly encouraged to think about their own research project and use their own data. The course provides the opportunity to discuss concrete problems of ‘doing research’ and come up with solutions


Each class will consist of a three-hour session. The instructor will lecture on the topic of the day, on which students are expected to read the recommended reading(s) indicated in the detailed syllabus below. Students will work with datasets guided by the instructor. The material for the course will be available in a shared folder, but students are encouraged to use their own data.


Project work (max 3,000 words) addressing a research question using quantitative data and analysing it with statistical tools.

Ungraded – pass/fail – for PhD students.

Graded on a 30-point scale for MA students.


Detailed programme and schedule

Note: additional material will be shared ahead of each class (also in the light of participants’ interests)

Session 1 – Linear regression: a recap

9 January 2024, 10.00 – 13.00


Agresti, Chapters 9-11


Session 2 – Models for binary outcomes (I)

23 January 2024, 10.00 – 13.00


Long & Freese, Chapter 5


Session 3 – Models for binary outcomes (II)

 30 January 2024, 10.00 – 13.00

Long & Freese, Chapter 6

Session 4 – Multinomial logit

6 February 2024, 10.00 – 13.00


Long & Freese, Chapter 8

Session 5 – Count models

27 February 2024, 10.00 – 13.00


Long & Freese, Chapter 9


Session 6 – Survival analysis

5 March 2024, 10.00 – 13.00

Cleves, M., Gould, W. W., & Y. V. Marchenko (2016). An Introduction to Survival Analysis Using STATA, STATA Press, Chapters 1-4; 8-9. 


Session 7 – Project work

12 March 2024, 11.00-13.00


King, G. (2006). Publication, Publication. PS: Political Science and Politics, Vol. XXXIX, No. 1, pp. 119-125.

King, G. Tomz, M. & J. Wittenberg (2000). Making the Most of Statistical Analyses: Improving Interpretation and Presentation. American Journal of Political Science, 44:2, pp. 341-355.

Obiettivi formativi

- Develop an advanced understanding of quantitative methodologies for the Social Sciences

- Appreciate and select the appropriate techniques to analyse quantitative datasets

- Use statistical software to analyse and interpret statistical analyses and, whenever possible, own data

Riferimenti bibliografici

Alan Agresti (2021). Statistical Methods for the Social Sciences, 5th edition. Pearson.

J. Scott Long and Jeremy Freese (2014). Regression Models for Categorical Dependent Variables Using Stata, Third Edition, STATA Press.

Background reading to start working with STATA:

Alan C. Acock (2023). A Gentle Introduction to Stata, Revised Sixth Edition, STATA Press.

For a comprehensive and up-to-date overview of the field:

Curini, L. & R. Franzese (2020). The SAGE Handbook of Political Science and International Relations. London: SAGE.