Foundations of Statistical Modelling for Social and Political Sciences
A.Y. 2026/2027
Learning objectives
This course offers an introduction to the foundational principles of statistical science, a discipline that underpins much of modern data analysis and decision-making. The focus is on providing a comprehensive understanding of the core concepts and methods that form the basis of statistical thinking and practice.
Designed for students in the social and political sciences, this course aims to equip participants with the essential tools and knowledge to effectively understand and apply statistical methods within their fields. Emphasis is placed on mastering the fundamentals of statistical science, which are crucial for analyzing and interpreting data rigorously and meaningfully.
The course combines theoretical lessons on statistical techniques with practical sessions that emphasize their empirical application using R software. Topics will primarily follow a frequentist approach, with introductory notions of Bayesian methods included to broaden students' perspectives.
Designed for students in the social and political sciences, this course aims to equip participants with the essential tools and knowledge to effectively understand and apply statistical methods within their fields. Emphasis is placed on mastering the fundamentals of statistical science, which are crucial for analyzing and interpreting data rigorously and meaningfully.
The course combines theoretical lessons on statistical techniques with practical sessions that emphasize their empirical application using R software. Topics will primarily follow a frequentist approach, with introductory notions of Bayesian methods included to broaden students' perspectives.
Expected learning outcomes
By the end of the course, students will have acquired a solid set of skills in quantitative research from both a theoretical and practical perspective. They will be able to conduct univariate, bivariate, and multivariate analyses, and apply the fundamental principles of statistical inference. Additionally, students will be proficient in using the statistical software R and capable of independently carrying out a research project.
Specifically, students will learn to:
· Enter and manage their own datasets for analysis;
· Identify appropriate statistical methods to address theory-driven research questions;
· Conduct their own analyses using R;
· Interpret the results of data analysis.
Specifically, students will learn to:
· Enter and manage their own datasets for analysis;
· Identify appropriate statistical methods to address theory-driven research questions;
· Conduct their own analyses using R;
· Interpret the results of data analysis.
Lesson period: First trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Course syllabus
During the theoretical and practical sessions will be covered the following topics
· Descriptive Statistics: Methods for summarizing and visualizing data to reveal patterns and insights.
· Probability Distributions: Theoretical foundations for understanding randomness and variability in data.
· Inferential Statistics: Techniques such as confidence intervals and hypothesis testing, which allow conclusions to be drawn from sample data.
· Modeling Approaches: Linear and generalized linear models.
· Descriptive Statistics: Methods for summarizing and visualizing data to reveal patterns and insights.
· Probability Distributions: Theoretical foundations for understanding randomness and variability in data.
· Inferential Statistics: Techniques such as confidence intervals and hypothesis testing, which allow conclusions to be drawn from sample data.
· Modeling Approaches: Linear and generalized linear models.
Prerequisites for admission
Students are expected to have a basic knowledge of calculus and a general understanding of descriptive statistics and probability theory. While prior programming experience is beneficial, it is not mandatory.
Teaching methods
The course will be held in person. Attendance is not mandatory, although it is strongly recommended in order to facilitate learning.
The course will be conducted through interactive lectures, during which theoretical issues will be discussed and practical cases presented. The aim is to work interactively with students, encouraging their participation and organizing moments of discussion and peer interaction. In addition to the lectures, some hours of lab exercises are scheduled, where the concepts presented in class will be applied using R software.
The course will be conducted through interactive lectures, during which theoretical issues will be discussed and practical cases presented. The aim is to work interactively with students, encouraging their participation and organizing moments of discussion and peer interaction. In addition to the lectures, some hours of lab exercises are scheduled, where the concepts presented in class will be applied using R software.
Teaching Resources
-Agresti, A., & Kateri, M. (2022). 'Foundations of Statistics for Data Scientists: With R and Python.' CRC Press.
- Agresti A. , Franklin C. & Klingenberg B. Statistics: The Art and Science of Learning from Data, Pearson Education Limited
- Instructor-prepared slides and notes available on the course platform (Ariel).
- Agresti A. , Franklin C. & Klingenberg B. Statistics: The Art and Science of Learning from Data, Pearson Education Limited
- Instructor-prepared slides and notes available on the course platform (Ariel).
Assessment methods and Criteria
There are two options for the exam:
Option A: Team work and written exam
Team Work:
Students will form groups (maximum 5 /6 people per group) to collect and analyse data relating to relevant recent problems.
Preparation of a classroom presentation is required, to be delivered in front of peers.
A detailed report, outlining the individual contributions of group members, is mandatory.
Scheduled classroom sessions will monitor and assess the progress of each group's work.
Written exam
The test will consist of two parts:
Part 1 - Open Question:
The test will feature 3 general questions related to the course material.
Maximum score 5 points
Part 2 - Multiple Choice Questions:
A set 5 multiple choice questions covering key concepts of the course.
Scoring for multiple choice questions:
+1 point for each correct answer
−0.25 points for each incorrect answer
0 points for unanswered questions
Students are allowed to bring a non-programmable calculator.
Duration: 60 minutes
Final scoring will comprise three components:
Assessment of team work (10 points)
Evaluation of the report (10 points)
Result from the written test (10 points)
Honors will be granted to students who not only achieve the highest scores but also display active and substantial engagement in the assigned activities.
Option B: Individual work and written exam
Individual work
Students are required to submit a report providing a detailed analysis of a case study of their choice. They may use data from the textbook, published articles, or public databases.
A short report, outlining the main results, is mandatory.
They will be required to prepare a presentation to be delivered, if possible, in class in front of their classmates or to the instructor.
Written exam
The test will consist of two parts:
Part 1 - Open Question:
The test will feature 3 general questions related to the course material.
Maximum score 5 points
Part 2 - Multiple Choice Questions:
A set of 5 multiple choice questions covering key concepts of the course.
Scoring for multiple choice questions:
+1 point for each correct answer
−0.25 points for each incorrect answer
0 points for unanswered questions
Students are allowed to bring a non-programmable calculator.
Duration: 60 minutes
Final scoring will comprise three components:
Assessment of oral presentation (10 points)
Evaluation of the report (10 points)
Result from the written test (10 points)
Honours will be awarded only if the maximum score is achieved and if the work developed and/or the presentation show elements of originality and demonstrate a high level of commitment.
Non-attending students may choose either of the two available options. If they choose Option A, they will be required to participate in the final in-class discussion of the group project.
Option A: Team work and written exam
Team Work:
Students will form groups (maximum 5 /6 people per group) to collect and analyse data relating to relevant recent problems.
Preparation of a classroom presentation is required, to be delivered in front of peers.
A detailed report, outlining the individual contributions of group members, is mandatory.
Scheduled classroom sessions will monitor and assess the progress of each group's work.
Written exam
The test will consist of two parts:
Part 1 - Open Question:
The test will feature 3 general questions related to the course material.
Maximum score 5 points
Part 2 - Multiple Choice Questions:
A set 5 multiple choice questions covering key concepts of the course.
Scoring for multiple choice questions:
+1 point for each correct answer
−0.25 points for each incorrect answer
0 points for unanswered questions
Students are allowed to bring a non-programmable calculator.
Duration: 60 minutes
Final scoring will comprise three components:
Assessment of team work (10 points)
Evaluation of the report (10 points)
Result from the written test (10 points)
Honors will be granted to students who not only achieve the highest scores but also display active and substantial engagement in the assigned activities.
Option B: Individual work and written exam
Individual work
Students are required to submit a report providing a detailed analysis of a case study of their choice. They may use data from the textbook, published articles, or public databases.
A short report, outlining the main results, is mandatory.
They will be required to prepare a presentation to be delivered, if possible, in class in front of their classmates or to the instructor.
Written exam
The test will consist of two parts:
Part 1 - Open Question:
The test will feature 3 general questions related to the course material.
Maximum score 5 points
Part 2 - Multiple Choice Questions:
A set of 5 multiple choice questions covering key concepts of the course.
Scoring for multiple choice questions:
+1 point for each correct answer
−0.25 points for each incorrect answer
0 points for unanswered questions
Students are allowed to bring a non-programmable calculator.
Duration: 60 minutes
Final scoring will comprise three components:
Assessment of oral presentation (10 points)
Evaluation of the report (10 points)
Result from the written test (10 points)
Honours will be awarded only if the maximum score is achieved and if the work developed and/or the presentation show elements of originality and demonstrate a high level of commitment.
Non-attending students may choose either of the two available options. If they choose Option A, they will be required to participate in the final in-class discussion of the group project.
STAT-03/B - Social statistics - University credits: 9
Lessons: 60 hours
Professor:
Tarantola Claudia
Shifts:
Turno
Professor:
Tarantola ClaudiaProfessor(s)
Reception:
Wednesday 9:30 a.m. to 12:30 p.m. (by appointment) or via teams (by appointment)
Via Conservatorio 7, office 34, (by appointment)