81769 - Method and Data Analysis Techniques (dispa)

Course Unit Page

  • Teacher Marco Albertini

  • Credits 8

  • Teaching Mode Traditional lectures

  • Language Italian

Academic Year 2018/2019

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 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 spreadsheets (Excel) and / or other 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. 

 

Other useful texts are: 

Argentin, Gianluca, La regressione multipla, cap. 2 in Antonio de Lillo et alii, Analisi multivariata per le scienze sociali, Milano, Pearson, 2007, pp. 13-53.

Blalock, Hubert M., Jr., Statistica per la ricerca sociale, Bologna, Il Mulino, 1984 (solo parte terza, Statistica induttiva, ossia capitoli 8, 9 10, 11 e 12, dedicati rispettivamente a: Introduzione alla statistica induttiva; La probabilità; Verifica delle ipotesi: la distribuzione binomiale; Tests relativi a medie e proporzioni in un solo campione; Stima puntuale e stima per intervallo).

Pisati, M. (2003) L'analisi dei dati. Tecniche quantitative per le scienze sociali. Bologna: il Mulino.

Sarti, Simone, La regressione logistica, cap. 3 in Antonio de Lillo et alii, Analisi multivariata per le scienze sociali, Milano, Pearson, 2007, pp. 55-90.

Terraneo, Marco, Studiare e controllare le relazioni: l’analisi bivariata e la terza variabile, cap. 7 in Antonio de Lillo e altri, Metodi e tecniche della ricerca sociale, Milano-Torino, Pearson, 2011, pp. 307-378.

Teaching methods

Face-to-face lessons and practical exercises. Attendance is strongly recommended.

 

Course materials available here: https://iol.unibo.it/course/view.php?id=34984

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.

Office hours

See the website of Marco Albertini