The first Crash Course Day, organized by the SISP Standing Group on Research Methods in Political Science (MetRiSP), will take place on Wednesday, 2 September 2026, at the University of Trento, the day before the start of the Annual Conference.
Designed as an intensive, practice-oriented learning experience, the event will offer participants the opportunity to strengthen methodological skills that are increasingly central to political science research. Three courses will be offered in English.
- Introduction to R, taught by Stefano Sangiovanni (University of Milan), provides an introduction to statistical programming, guiding participants through data management, visualization, and core analytical techniques using R.
- Spatial Data Analysis, taught by Jessica Di Salvatore (University of Florence), focuses on the use of spatial data in political research, introducing tools and methods to analyse geographically connected phenomena and events.
- Social and Discourse Network Analysis, taught by Laura Mastroianni (University of Bologna), explores how social networks can be used to understand political interactions, combining theoretical insights with practical applications.
The crash courses are designed to be interactive and accessible to participants with different levels of prior experience.
Course Descriptions
Intro to R
Instructor: Stefano Sangiovanni, University of Milan
This course provides a practical introduction to R and data analysis in 2026. Its aim is to offer a broad overview of how R can be used in political science research and to highlight the opportunities it offers to researchers, adopting a contemporary approach that integrates artificial intelligence tools and situates R within a broader research ecosystem.
The course is structured in three parts. The first introduces the basics of R and RStudio, covering arithmetic and logical operators, key data structures (variables, vectors, data frames), and how to create, modify, and filter them. The second part focuses on the manipulation and analysis of real-world data, using the results of the 2022 Italian elections. The third part is dedicated to data visualization with ggplot2, introducing its layered grammar and main types of plots.
The course is designed for participants with no prior experience in R. No background in quantitative methods, statistical software, or programming is required.
Spatial Data Analysis
Instructor: Jessica Di Salvatore, University of Florence
What does the spatial dimension add to our understanding of political and social phenomena? This course is designed for social and political scientists interested in leveraging spatial data to enrich their analyses, whether by using existing georeferenced data or creating their own.
Participants will also learn how to identify and model spatial dependencies, which characterize most social phenomena. The course has three main objectives: (i) building datasets based on the geographical/spatial characteristics of the phenomena of interest; (ii) visualizing spatial patterns; and (iii) identifying sources of spatial interdependence.
The first part of the course focuses on the use of GIS tools to construct spatial datasets containing geographical variables (e.g. distances) and to merge (and, if necessary, geocode) different types of location-based data. The second part is dedicated to mapping tools. The third part introduces spatial interdependence and how to address it.
Familiarity with R, OLS regression, and its main assumptions is required.
Social and Discourse Network Analysis
Instructor: Laura Mastroianni, University of Bologna
Relational approaches are increasingly used to study social and political phenomena, based on the idea that actors, ideas, and preferences can be understood through the relationships that connect them. This intensive and interactive course introduces the theoretical foundations and analytical potential of relational methods in political science, with a focus on Discourse Network Analysis (DNA) and Social Network Analysis (SNA).
The course aims to provide participants with an understanding of the theoretical bases of relational approaches, introduce key concepts and techniques of SNA and DNA, and develop skills for constructing and analysing network data. Participants are also encouraged to reflect on how these methods can be applied to their own research interests.
No prior experience in network analysis is required; familiarity with RStudio is helpful but not necessary.
Programme
9:00–9:30 Welcome
9:30–11:00 Course 1: Introduction to R (Part 1)
11:00–11:30 Coffee Break
11:30–13:00 Course 1: Introduction to R (Part 2)
13:00–14:00 Lunch Break
14:00–15:30
Course 2: Spatial Data Analysis /
Course 3: Social and Discourse Network Analysis (Part 1)
15:30–16:00 Coffee Break
16:00–17:30
Course 2: Spatial Data Analysis /
Course 3: Social and Discourse Network Analysis (Part 2)
Fees
One course (Introduction to R or Spatial Data Analysis or Social and Discourse Network Analysis)
- PhD students and research fellows: €20
- Researchers: €40
- Associate and full professors: €60
Two courses (Introduction to R + Spatial Data Analysis or Introduction to R + Social and Discourse Network Analysis)
- PhD students and research fellows: €30
- Researchers: €60
- Associate and full professors: €90
Courses are free of charge for recipients of SISP mini-grants.
Registration
Registration will take place exclusively through MySISP from 15 April to 31 July 2026, uploading a proof of payment in the registration form.
- Registration for Introduction to R (max 40 participants)
- Registration for Spatial Data Analysis (max 20 participants)
- Registration for Social and Discourse Network Analysis (max 20 participants)
To complete the registration, a bank transfer must be made by 31 July to the following account:
IBAN: IT59L0501801000000020000657
SWIFT/BIC: ETICIT22XXX
Account holder: Società Italiana di Scienza Politica
Bank: Banca Etica
