Methodologies for the Social Sciences II: Quantitative

Periodo di svolgimento

da Gennaio 2021 a Febbraio 2021
Ore del corso: 20
Ore dei docenti responsabili: 20

Modalità d'esame

• Prova scritta

Prerequisiti

Compulsory for the 1st year students of the PhD Programme in "Political Science and Sociology"

Compulsory for the 1st year students of the PhD Programme in "Transnational Governance"

Optional for the 4th and 5th year students of the MA Programme in "Political and Social Sciences"

Programma

The course consists in an introduction to quantitative data analysis for social and political science research,  with a particular focus on linear regression. After a more  general introduction on the nature of quantitative research, it will start from the basics of quantitative analysis: types of variables, data sets, simple descriptive statistics. It will then cover all the main aspects of regression analysis: its assumptions, the estimation technique, its application to concrete research problems, the presentation of results. The course will conclude by giving an overview of other types of regressions that participants might need to use in their future research (particularly discrete choice and nested models). At the end of this course, participants will be able to select the appropriate method for their research question, carry out basic quantitative analysis (including descriptive tables and graphs) as well as linear
regressions, and they will be able to meaningfully interpret the results of these methods. The course combines the theoretical study of basic statistical analysis with applied, practical exercises using the statistical programme Stata.

Obiettivi formativi

By the end of this module, students will be able to:

- understand the key issues in quantitative analysis and the underlying logic of regression techniques;

- meaningfully interpret the results of regression models;

- use the statistical software STATA for descriptive and inferential analysis

- apply the techniques and methods explored in the module to their own PhD project (if relevant)

Riferimenti bibliografici

(1)  Lewis-Beck, C. and LewisBeck,M. (2016) Applied Regression: An Introduction.Thousand Oaks, California: SAGE

(2)  UCLA, Institute for Digital Research and Education, STATA Learning Modules, https://stats.idre.ucla.edu/stata/modules/