91212 - METODO E TECNICHE DELL'ANALISI DEI DATI (DISPA)

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Reduced inequalities

Academic Year 2021/2022

Learning outcomes

The course addresses statistical data analysis techniques. Students are expected to have previously acquired basic skills in social and political research methodology. Upon successfully completing the course, students should be proficient in the basic techniques for bivariate descriptive analysis of data collected in data matrices; understand the main principles of statistical inference; be familiar with the key features of multivariate statistical analysis; be able to interpret results of multivariate models; grasp the chief sampling techniques; can critically evaluate data analyses performed by other scholars; knows how to access statistical data sources.

Course contents

The course will probably be organized as it follows: lectures (16 hours) aim to introduce students to the core tenets of the discipline. Practical exercises (12 hours) aim to provide occasions for in-depth discussions of class materials and exercises. For the practical exercises section of the course, students will be divided in two groups.  

The course provides an in-depth overview of statistical analysis techniques, aims to promote students' ability to interpret and critically evaluate statistical information, and provides specific skills for the autonomous processing of information gathered in a data matrix. By the end of the course, student will be able to read and understand articles in specialized publications containing statistical analysis results; evaluate statistical summaries and processing of census or sampling data; autonomously apply a selection of statistical analysis techniques for the description of economic, political and social phenomena; know the basics of inferential statistics. In particular, teaching will focus on bivariate and multivariate analysis techniques and will also have a practical component focusing on the use of software dedicated to data processing.

Students should already be well-versed in sociological / political science methodology and basic elements of statistics. If the student is not in possession of suck skills knowledge, attendance of Social and Political Research Methodology (course no. 78074) is strongly recommended.

As to the basic elements of statistics, at the very least a careful reading of the following text is required: P. Corbetta, G. Gasperoni and M. Pisati, Statistica per la ricerca sociale, Bologna, Il Mulino, 2001 (chapters 1-3) .

Readings/Bibliography

Reference texts (for both attending and non-attending students) are the following:

Agresti, A. e Finlay, B. (2015) Metodi statistici di base e avanzati per le scienze sociali. Milano: Pearson.

Corbetta, Piergiorgio, Giancarlo Gasperoni e Maurizio Pisati, Statistica per la ricerca sociale, Bologna, Il Mulino, 2001 (capitoli 4-10).

Other texts will be recommended before the beginning of the course. All materials utilized in class, and more, will be available on the virtuale platform. 

Teaching methods

Face-to-face/on line lessons and practical exercises.

More details on the practical organization of lessons will be communicated in class and are available on the virtuale.unibo.it platform. 

The course will use the e.learning platform iol, students are advised to register on the virtuale platform of UniBo. 

Assessment methods

The exam is administered in exclusively written form. The only valid mark is the one achieved in the most recent attempt to pass the exam. A candidate who doesn't participate in an exam for which he/she has registered cannot participate in the following exam session.

 

In light of the current health emergency the exam will be administered in two steps: in the first step a written exam via Zoom/MS TEAMS and the eol platform, in the second step an oral exam. 

Please note: students with an *even* immatriculation number must take the exam with Prof. Gasperoni. No exceptions.

Teaching tools

virtuale.unibo.it platform + MS teams 

Stata lab. 

Office hours

See the website of Marco Albertini

See the website of