Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

1° Year

ModulesCreditsTAFSSD

2° Year  activated in the A.Y. 2023/2024

ModulesCreditsTAFSSD
activated in the A.Y. 2023/2024
ModulesCreditsTAFSSD
Modules Credits TAF SSD
Between the years: 1°- 2°
1 module among the following (a.a. 2023/24: Data protection in business organizations not activated)
6
C
IUS/17
Between the years: 1°- 2°
2 modules among the following (a.a. 2023/24: Statistical methods for business intelligence not activated)
Between the years: 1°- 2°
2 modules among the following (a.a. 2023/24: Complex systems and social physics not activated)
Between the years: 1°- 2°
2 modules among the following
Between the years: 1°- 2°

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S009087

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

SPS/07 - GENERAL SOCIOLOGY

Period

Semester 2 dal Mar 6, 2023 al Jun 16, 2023.

Learning objectives

The course aims to offer students an overview of the main paradigms of social research, in order to identify the solutions proposed by these paradigms for ontological, epistemological and methodological questions related to social research. The course is also aimed at providing students with a general framework of the main quantitative and qualitative research techniques, linking them to the specific research questions for which their application is particularly relevant. Finally, the course aims to provide a presentation of the Social Network Analysis (SNA) techniques, in order to train students to the relational analysis of social reality. At the end of the course the student has to show to have acquired the following skills: -ability to identify the most effective social research strategy to deal with a cognitive problem -ability to use, with competence and appropriate language, the techniques of Social Network Analysis (SNA), finalizing them to the operational definition of social reality as a network of relationships and to the identification of the mechanisms that take place in it.

Prerequisites and basic notions

There are no prerequisites

Program

The course will provide students with the ability to learn and apply the qualitative research method, quantitative research techniques and SNA in the field of social research.
The program is divided into the following modules:
Qualitative social research
Quantitative social research
SNA
Big data and social research
Each research method will be dealt with a theoretical and practical point of view. They will be applied to analyse specific empirical research questions.
To gain experience with the practical application of these techniques, students will practice using the software most commonly applied in social research.
The course will combine theoretical lectures, data analysis activities and practice in the interpretation of the outputs obtained.

Bibliography

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

All lectures will be delivered face-to-face.

Learning assessment procedures

Written/Oral

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

The final assessment mark will combine the following three elements:
1) Practical activities of data analysis and interpretation of the outputs (30%)
2) A written assignment dealing with data analysis (min. 2000 words) (30%)
3) Oral examination (40%)

Exam language

English