Statistics and Data Analysis
A.Y. 2026/2027
Learning objectives
The course aims to provide students with a solid conceptual and operational foundation in statistics as applied to the social sciences. It introduces the logic of theory-driven quantitative analysis, with the goal of developing competencies in the use of fundamental statistical tools for empirical research. Particular attention is devoted not only to understanding quantitative methods but also to their correct application in the analysis of complex social phenomena.
Expected learning outcomes
By the end of the course, students will be able to:
Knowledge and Understanding
- Identify and describe the main types of variables and the most appropriate techniques for univariate and bivariate analysis.
- Explain the logic of descriptive and inferential statistics, highlighting their differences and complementarity.
- Describe the fundamental principles of simple and multiple linear regression.
Applying Knowledge and Understanding
- Apply univariate and bivariate analysis techniques to summarize, represent, and interpret quantitative data in the social sciences.
- Calculate and interpret measures of central tendency and dispersion, point estimates, and confidence intervals.
- Conduct simple and multiple linear regression analyses and correctly interpret the results.
- For attending students, independently use the statistical software Stata to manage and analyze quantitative datasets.
Judgement Autonomy
- Select and justify the most appropriate statistical methods based on the research question and the nature of the data.
- Critically evaluate the results of quantitative analyses in the context of empirical problems.
Communication Skills
- Present the results of quantitative analyses in a clear, coherent, and structured manner using appropriate technical language.
- Interpret and comment on graphs, tables, and statistical outputs, effectively highlighting key results in academic or professional settings.
Knowledge and Understanding
- Identify and describe the main types of variables and the most appropriate techniques for univariate and bivariate analysis.
- Explain the logic of descriptive and inferential statistics, highlighting their differences and complementarity.
- Describe the fundamental principles of simple and multiple linear regression.
Applying Knowledge and Understanding
- Apply univariate and bivariate analysis techniques to summarize, represent, and interpret quantitative data in the social sciences.
- Calculate and interpret measures of central tendency and dispersion, point estimates, and confidence intervals.
- Conduct simple and multiple linear regression analyses and correctly interpret the results.
- For attending students, independently use the statistical software Stata to manage and analyze quantitative datasets.
Judgement Autonomy
- Select and justify the most appropriate statistical methods based on the research question and the nature of the data.
- Critically evaluate the results of quantitative analyses in the context of empirical problems.
Communication Skills
- Present the results of quantitative analyses in a clear, coherent, and structured manner using appropriate technical language.
- Interpret and comment on graphs, tables, and statistical outputs, effectively highlighting key results in academic or professional settings.
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.
For attending students, in parallel with theoretical lessons, significant time will be devoted to practical application using the Stata software for data management and analysis, with particular emphasis on the interpretation of the results.
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.
For attending students, in parallel with theoretical lessons, significant time will be devoted to practical application using the Stata software for data management and analysis, with particular emphasis on the interpretation of the results.
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. The course is also complemented by 10 hours of lab sessions aimed at consolidating the topics covered during the lectures, further developing students' proficiency in the use of the statistical software Stata, and supporting them in the preparation of their final research report.
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.
- Research report
Starting from a research question, students are required to independently develop an empirical analysis using the methods and tools acquired during the course. The report should include the formulation of the research question, a description of the data used, the methodological choices, the data analysis, and the interpretation of the results. The assignment aims to assess the ability to independently apply the acquired skills, as well as to correctly interpret and communicate the results of a quantitative analysis.
For non-attending students:
Assessment is based exclusively on a written examination consisting of multiple-choice questions, open-ended questions, and practical exercises. The examination is designed to assess students' understanding of the theoretical concepts and their ability to apply them to the analysis of quantitative data. It covers the entire course syllabus and the prescribed textbook.
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.
- Research report
Starting from a research question, students are required to independently develop an empirical analysis using the methods and tools acquired during the course. The report should include the formulation of the research question, a description of the data used, the methodological choices, the data analysis, and the interpretation of the results. The assignment aims to assess the ability to independently apply the acquired skills, as well as to correctly interpret and communicate the results of a quantitative analysis.
For non-attending students:
Assessment is based exclusively on a written examination consisting of multiple-choice questions, open-ended questions, and practical exercises. The examination is designed to assess students' understanding of the theoretical concepts and their ability to apply them to the analysis of quantitative data. It covers the entire course syllabus and the prescribed textbook.
STAT-01/A - Statistics - University credits: 9
Asynchronous teaching: 10 hours
Synchronous online teaching: 20 hours
Lessons: 30 hours
Synchronous online teaching: 20 hours
Lessons: 30 hours
Professor:
Cantalini Stefano
Professor(s)
Reception:
Thursday, 9.30-12.30
Microsoft Teams (by appointment)