Methodologies for the Social Sciences II: Quantitative (introductive course)

Course description:

This course is meant to provide a general introduction to quantitative data analysis for social and political science research. It assumes no prior (or very basic) knowledge of statistical methods and quantitative techniques. It will combine theoretical foundations of quantitative designs with a practical, hands-on approach— for this purpose, the statistical software STATA shall be used.

The course will start from the basics of quantitative analysis, covering key concepts and features, types of variables and datasets. After that, it will address descriptive statistics. It will then deal with the main aspects of linear regression analysis: assumptions, estimations, practical application to research questions and examples, presentation of results and interpretation. The course will conclude by giving an overview of other types of regressions that participants might need to use in their future research. At the end of this course, participants will be able to understand the theoretical background of the most basic statistical techniques and select the appropriate method for their research question; learn how to organize a dataset and perform basic quantitative analysis (including descriptive tables and graphs) as well as linear regressions; they will be able to meaningfully interpret the results of these methods; and how to use the software STATA for data analysis.

Course format:

Most classes will consist of 3-hour sessions. Readings are mandatory before each sessions. All classes will have a theory part delivered by the instructor (lecture format), but will also have a lab part where students will work in small groups and learn to run their own statistical analyses with STATA (hands-on format). Materials for the course will be available in a shared folder.

Assessment:

Assessment (pass/fail for PhD students; graded on a 30-point scale for MA students) will be based on a take-home exam after the course is over— deadline to be confirmed. The exam will consist of short questions covering key contents of the course and a practical exercise with STATA. It will not require any knowledge of calculus or mathematics beyond simple arithmetic.

Detailed content

Session 1: May 2nd, 10am-1pm – Doing quantitative research: an introduction

Variables and types of variables. Examining and reading datasets: meeting Stata.

Readings: Treiman (2009), chap. 1; Acock (2018), chap. 1-3.

Session 2: May 3rd, 2pm-5pm – Descriptive statistics

Exploring the relationship among variables. Presenting tables and graphs. Variable transformation and correlation. From descriptive to inferential statistics.

Readings: Treiman (2009), chap. 5; Acock (2018), chap. 5.

Session 3: May 6th, 2pm-5pm – Bivariate regression

Fitting a straight line. Bivariate regression, OLS with continuous dependent variable. Estimation and interpretation of coefficients, significance tests and standard errors. Goodness of fit in a linear regression.

Readings: Lewis-Beck & Lewis Beck (2016), chap. 2; Acock (2018), chap. 6.

Session 4: May 10th, 10am-1pm – Multivariate regression I

Goodness of fit. Checking the assumptions of (linear) regressions: linearity, homoscedasticity, independence, normal distribution, no multicollinearity

Readings: Lewis-Beck & Lewis Beck (2016), chap. 3.

Session 5: May 16th, 10am-1pm – Multivariate regression II

Functional forms: interpreting and presenting regression results. Outliers, truncation, regression fallacy, ecological fallacy. Omitted, suppressor and intervening variables. Interactions.

Readings: Lewis-Beck & Lewis Beck (2016), chap. 3-4.

Session 6: May 27th, 10am-12pm – Extensions of the linear regression model

Introduction to discrete choice models: the logit model.

Readings: Treiman (2009), chap. 13-14.

Session 7: May 30th, 10am-1pm – Taking stock, moving forward

Reflect on your own research project(s), and what research design is the most appropriate. Where to find the data? Reflections on causality, potentials of quantitative designs and further techniques.

Readings: Treiman (2009), chap. 16.