Methodologies for the Social Sciences II: Quantitative (introductive course)
Prerequisites
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 the 5th year students of the MA Programme in "Political and Social Sciences"
Programme
The course consists in an introduction to quantitative data analysis for social and political science research, with a particular focus on linear regression. The course 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 a basic and preliminary overview of other types of regressions that participants might need to use in their future research (such as regression models for categorical variables). 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 practical exercise with the statistical software STATA.
Course format
Each class will consist of a three-hour session. The instructor will lecture on the topic of the day, on which students are expected to read the recommended reading(s) indicated below. On each topic, participants will need to solve an exercise assigned by the instructor, using the statistical software STATA. The material for the course will be available in a shared folder.
Detailed structure and schedule
Session 1 – 10 January 2023 (14.00-17.00)
Doing quantitative research: introduction. Variables and types of variables. Examining and reading data sets.
Session 2 – 11 January 2023 (10.00 -13.00)
Descriptive statistics. Describing datasets. Presenting tables and graphs. Exploring the relationship among variables.
Correlation.
Session 3 – 19 January 2023 (14.00 - 17.00)
Bivariate regression: Fitting a straight line Fitting a straight line. Bivariate regression. Estimation and interpretation of coefficients and significance tests. Measuring the goodness of fit of a linear regression.
Session 4 – 23 January 2023 (14.00 - 17.00)
Multivariate regression: an introduction. Interpreting and assessing the goodness of fit of multivariate regression. Checking the assumptions of linear regressions: omitted variables, functional forms, outliers, normality of residuals.
Session 5 – 30 January 2023 (14.00 - 17.00)
Multivariate regression: interpretation and presentation
Checking the assumptions of linear regressions: heteroskedasticity and multicollinearity. Presenting regression results.
Session 6 - 3 February 2023 (14.00 - 17.00)
Extensions of the linear regression model
Introduction to discrete choice models: the logit model.
Session 7 - 15 February 2023 (10.00 - 12.00)
Final thoughts and future directions
Reflect on your own research project and data, and what research design is the most appropriate.
Educational aims
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 basic inferential analysis
- apply the techniques and methods explored in the module to their own PhD project (if relevant)
Bibliographical references
Acock, A. C. (2018). A Gentle Introduction to STATA. College Station, Texas: Stata Press
Lewis-Beck, C. and Lewis Beck, M. (2016) Applied Regression: An Introduction.
Specific readings will be provided in class