Luiss Guido Carli | PhD in Politics | Quantitative Methods Masterclass

Academic year 2026-2027 - Second year - First semester

Programme PhD in Politics — second year
Period First semester, 2026-2027
Instructor Francesco Visconti
Field Methods of Political Research
Software STATA (on your own laptop)

Course description

This course offers advanced training in quantitative methods applied to research in the social sciences, with particular reference to political science and political sociology. Designed as an operational masterclass, it aims to strengthen doctoral candidates’ analytical skills through the direct application of statistical tools and reflection on methodological choices in their own research projects.

The course is conceived as a cumulative extension of the first-year course in Quantitative Methods: it starts again from regression and progressively complicates what students already know — moving from “controlling for” to identification, from exploratory to confirmatory measurement models, from fixed effects to difference-in-differences, and from the experimental benchmark to instrumental variables — before closing on the integration of quantitative and qualitative evidence.

Learning outcomes

Knowledge and understanding

Students will acquire advanced knowledge of quantitative techniques applied to the social sciences, with a particular focus on the practical use of statistical tools and critical reflection on methodological choices in their own research projects. At the end of the course students will be familiar with:

  1. the conditions under which a regression model can be given a causal interpretation;
  2. measurement models for latent constructs (CFA, SEM, measurement invariance);
  3. the identifying logic of experiments, difference-in-differences and instrumental variables;
  4. the main strategies for integrating quantitative and qualitative methods.

Applying knowledge and understanding

Students will be able to understand advanced quantitative methods relevant to their doctoral dissertations or research projects, and to specify, estimate, diagnose and interpret in STATA a measurement model, a difference-in-differences design and an instrumental variables estimation, reporting the results according to disciplinary standards.

Making judgements

Through the discussion of published applications and replications, students will learn to assess the credibility of others’ identifying assumptions and to choose knowingly the analytical strategy best suited to their own research question, recognising when the available data do not support a causal answer.

Communication skills

Students will improve their ability to present and discuss the results of their quantitative analyses, refining oral and written communication skills with particular attention to clarity and accuracy in data presentation.

Learning skills

The course fosters autonomy in methodological updating, training students to read recent methodological literature, to consult software documentation and to work with replicable code.

Entry requirements

Successful completion of the first-year course Quantitative Methods (second semester). Familiarity with multiple OLS regression, models for categorical dependent variables, panel data with fixed and random effects, and exploratory factor analysis is assumed.

Students are required to use STATA on their own laptop.

Teaching method

Each two-hour session combines three components:

  1. a lecture on the conceptual foundations of the method;
  2. a discussion of an application;
  3. a guided STATA lab.

The approach is replication-based: students work with data and do-files they can reuse on their own materials. Each technique is introduced from a substantive research problem. Readings marked as Optional are for further study and are not required for class discussion.

Course outline

Session Topic
1 Beyond control variables: regression, identification and selection on observables
2 Measuring concepts: from EFA to CFA, SEM and measurement equivalence
3 The experimental benchmark: experiments, survey experiments and conjoint designs
4 From fixed effects models to difference-in-differences
5 Instrumental variables: endogeneity, relevance, exclusion
6 From numbers to cases: multi-method research, mechanisms and transparency

Reference books

The course relies on papers and chapters from various authors. The required readings for each session are made available in advance. Two core texts are freely available online:

  • Huntington-Klein, N. (2022). The Effect: An Introduction to Research Design and Causality. Boca Raton: CRC Press. Open access: https://theeffectbook.net
  • Cunningham, S. (2021). Causal Inference: The Mixtape. New Haven: Yale University Press. Open access: https://mixtape.scunning.com

Further reference texts:

  • Acock, A. C. (2013). Discovering Structural Equation Modeling Using Stata. College Station: Stata Press.
  • Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling (5th ed.). New York: Guilford Press.
  • Angrist, J. D., & Pischke, J.-S. (2015). Mastering ’Metrics: The Path from Cause to Effect. Princeton: Princeton University Press.
  • Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton: Princeton University Press.
  • Druckman, J. N., & Green, D. P. (eds.) (2021). Advances in Experimental Political Science. Cambridge: Cambridge University Press.
  • Goertz, G. (2017). Multimethod Research, Causal Mechanisms, and Case Studies: An Integrated Approach. Princeton: Princeton University Press.
  • StataCorp (2025). Stata Structural Equation Modeling Reference Manual. College Station: Stata Press.

Academic honesty

Any student who commits an act of academic dishonesty will receive a failing grade on the work in which the dishonesty occurred. In addition, acts of academic dishonesty, irrespective of the weight of the assignment, may result in the student receiving a failing grade in the course. Instances of academic dishonesty will be reported to the Director of the PhD programme.