OVERVIEW

The teaching introduces students to the fundamental tools of statistical analysis in the social sciences, with a special focus on politics and international relations. During the lessons, statistical software is widely used, giving the course a more operational rather than theoretical approach.

AIMS AND CONTENT

LEARNING OUTCOMES

The teaching provides the students with the basic notions of the statistical methods useful for the study of political science and international relations. The students will be able to produce statistical reports with data tables, graphs and more complex analyses such as multiple regression. The teaching has an application-oriented approach, and several hours will be devoted to the introduction to data analysis software such as Microsoft Excel and IBM SPSS".

AIMS AND LEARNING OUTCOMES

• Knowledge and understanding Students acquire adequate knowledge of the principles of measuring and statistically analysing political phenomena. Students are able to import data into statistical analysis software, produce tabular and graphical representations, summarize them using the main descriptive statistics and analyze the relationship between two or more variables.
• Ability to apply knowledge and understanding Students are able to find the data necessary to understand the political phenomena and to replicate the indices studied during the course on new data or to build new ones, also in a historical perspective and international comparison.
• Autonomy of judgement Students are able to assess the quality of data and the quality of the research reports available online.
• Communication skills Students acquire the fundamental statistical vocabulary to communicate clearly and unambiguously with specialist and non-specialist interlocutors. In particular, they learn to interpret data tables and graphic representations typical of the economic, social and political sciences, especially when produced by official statistical bodies.
• Learning skills Students, having had the opportunity to know in detail many official surveys, are able to assess the quality of the data they will use during their studies and professional career.

PREREQUISITES

None

TEACHING METHODS

Face-to-face lectures.

SYLLABUS/CONTENT

1. Introduction to statistical methodology
2. Sampling and measurement
3. Descriptive statistics
4. Probability distributions
5. Statistical inference: estimation
6. Statistical inference: significant tests
7. Comparison of two groups
8. Linear regression and correlation

RECOMMENDED READING/BIBLIOGRAPHY

Agresti A. (2022). Statistical methods for the social sciences, 6th edition, Pearson.

This is the list of chapters and paragraphs included in the teaching program:

• Chapter 1: all the pages
• Chapter 2: all the pages
• Chapter 3: all the pages
• Chapter 4: all the pages
• Chapter 5: skip section 5.5
• Chapter 6: only sections 6.1, 6.2, 6.3
• Chapter 7: only sections 7.1, 7.2, 7.3
• Chapter 9: 9.1, 9.2, 9.3 (excluded "Conditional variation tends to be less than marginal variation"), 9.4, 9.5 (excluded confidence intervals), 9.6

Exercises

• Chapter 3: 3.1 - 3.53 excluded: 3.3, 3.6, 3.7, 3.11, 3.12, 3.33 and 3.43
• Chapter 4: 4.1 - 4.36 excluded: 4.18, 4.26 - 4.28, 4.32
• Chapter 5: 5.1 - 5.32 excluded: 5.13, 5.30
• Chapter 6: 6.1 - 6.26
• Chapter 7: 7.1 - 7.32
• Chapter 9: 9.1 - 9.3, 9.8, 9.11-9.14, 9.16 - 9.19, 9.21-9.23, 9.27, 9.32, 9.34, 9.36, 9.50-9.54, 9.57-9.61

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

2nd semester

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Written open book exam with 3 theoretical questions and 3 exercises (two from the book and one brand new).

ASSESSMENT METHODS

The written exam assesses the knowledge acquired by the student on the statistical methods discussed during the lessons.

FURTHER INFORMATION

There are no variations in the program or assessment methods for non-attending students. However, these students are encouraged to contact their reference teacher.

Students with certification of Specific Learning Disorders (SLD), disabilities, or other special educational needs are advised to contact both the coordinator, Prof. Aristide Canepa (aristide.canepa@unige.it), and the course instructor at the beginning of the course to agree on teaching and examination methods that, while respecting the course objectives, take into account individual learning methods and provide suitable compensatory tools. For further information, please refer to the webpage: https://unige.it/disabilita-dsa/studenti-disabilità-informazioni-utili