Quantitative Methods and Statistics for the Social Sciences
A.Y. 2025/2026
Learning objectives
To provide students with a solid foundation of fundamental statistical tools aimed not only at understanding but also at the proper application of quantitative methodologies for analyzing and solving complex problems in the social sciences.
Expected learning outcomes
At the end of the course a student will have acquired the appropriate terminology and will have learned the main tools of descriptive statistics (construction of indices, tables and graphs and interpretation of the same) and of inferential statistics (point estimation, confidence intervals and hypothesis testing).
In particular, he/she will be able to apply the right statistical technique to analyse data and to solve common real-life problems. He/she will be able to construct and read frequency tables and to interpret the most common statistical indices; to calculate and to interpret point estimates and confidence intervals and to test the most common statistical hypotheses. Finally, he/she will be able to perform a simple linear regression through a statistical software and to interpret the output.
In particular, he/she will be able to apply the right statistical technique to analyse data and to solve common real-life problems. He/she will be able to construct and read frequency tables and to interpret the most common statistical indices; to calculate and to interpret point estimates and confidence intervals and to test the most common statistical hypotheses. Finally, he/she will be able to perform a simple linear regression through a statistical software and to interpret the output.
Lesson period: Third 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
Third trimester
Course syllabus
The course provides a comprehensive introduction to statistics and quantitative analysis in the social sciences, with attention to both theoretical concepts and practical applications. It begins with the foundations of quantitative research, including the phases of the research process, the definition of the unit of analysis, and the distinction between cases and variables. Different types of variables will then be illustrated, along with an introduction to descriptive and inferential statistics.
This is followed by univariate analysis, focusing on frequency tables, measures of central tendency, and variability. The transformation of variables will also be addressed as an essential step in data preparation.
The course then introduces the core concepts of statistical inference, including sampling estimation, standard error, and confidence intervals. Attention then shifts to bivariate analysis, covering tools such as cross-tabulations, mean comparisons, analysis of variance (ANOVA), and correlation.
The course concludes with an introduction to the principles of linear regression, both in its simple form—useful for analyzing the relationship between two variables—and in its multiple form—used to study the relationships between several variables, thus moving from bivariate to multivariate analysis.
In parallel with theoretical lessons, significant time will be devoted to practical application using the Stata software for data management and analysis.
This is followed by univariate analysis, focusing on frequency tables, measures of central tendency, and variability. The transformation of variables will also be addressed as an essential step in data preparation.
The course then introduces the core concepts of statistical inference, including sampling estimation, standard error, and confidence intervals. Attention then shifts to bivariate analysis, covering tools such as cross-tabulations, mean comparisons, analysis of variance (ANOVA), and correlation.
The course concludes with an introduction to the principles of linear regression, both in its simple form—useful for analyzing the relationship between two variables—and in its multiple form—used to study the relationships between several variables, thus moving from bivariate to multivariate analysis.
In parallel with theoretical lessons, significant time will be devoted to practical application using the Stata software for data management and analysis.
Prerequisites for admission
There are no specific prerequisites. However, familiarity with basic mathematical concepts, including equations and algebraic expressions, is recommended.
Teaching methods
The course is delivered in a blended learning format, combining in-person lectures, synchronous online sessions, and asynchronous online content. It includes both traditional lectures and practical exercises—guided and individual—using the Stata statistical software.
Teaching Resources
Corbetta, P., Gasperoni, G., & Pisati, M. (2001). Statistica per la ricerca sociale. Il Mulino. Chapters: 1, 2, 3, 4, 5, 6, 7, 8, 10.
Additional materials, particularly those related to practical exercises using Stata, will be made available on the course's MyAriel platform.
Additional materials, particularly those related to practical exercises using Stata, will be made available on the course's MyAriel platform.
Assessment methods and Criteria
For attending students:
Assessment is based on two components:
- Written exam
Includes multiple-choice questions, open-ended questions, and exercises. The exam is designed to test understanding of theoretical concepts and their application.
- Practical test using Stata
Starting from a research question, students will be required to complete a series of tasks using Stata (e.g., variable transformation, univariate and bivariate analysis, simple linear regression). The test evaluates the operational skills developed during the course.
For non-attending students:
Assessment will consist of a single written exam based on the textbook, including multiple-choice questions, open-ended questions, and exercises.
Assessment is based on two components:
- Written exam
Includes multiple-choice questions, open-ended questions, and exercises. The exam is designed to test understanding of theoretical concepts and their application.
- Practical test using Stata
Starting from a research question, students will be required to complete a series of tasks using Stata (e.g., variable transformation, univariate and bivariate analysis, simple linear regression). The test evaluates the operational skills developed during the course.
For non-attending students:
Assessment will consist of a single written exam based on the textbook, including multiple-choice questions, open-ended questions, and exercises.
SECS-S/01 - STATISTICS - University credits: 9
: 10 hours
: 20 hours
Lessons: 30 hours
: 20 hours
Lessons: 30 hours
Professor:
Cantalini Stefano
Professor(s)
Reception:
Thursday, 9.30-12.30
Microsoft Teams (by appointment)