Магистратура 2022/2023

Методология и методы политических исследований

Статус: Курс по выбору (Политика. Экономика. Философия / Politics. Economics. Philosophy)

Направление: 41.04.04. Политология

Кто читает: Департамент политики и управления

Где читается: Факультет социальных наук

Когда читается: 1-й курс, 3, 4 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Седашов Евгений Александрович

Прогр. обучения: Политика. Экономика. Философия

Язык: английский

Кредиты: 6

Контактные часы: 64

Course Syllabus

Abstract

This course serves as an introduction to quantitative political methodology. We will first cover the general issues related to research design in political science. We will discuss problems of measurement and operationalization, validity and reliability of measurements, and the basics of writing research papers. After that we will proceed to the discussion of statistical methods and linear regression. We will start from first principles and gradually build skills required for thorough understanding of quantitative methods used in modern political science. Finally, we will also cover some of the topics from causal inference. The class strives to maintain the balance between building theoretical understanding and practical implementation of specific methods. I strongly believe that one is impossible without the other. On the one hand, understanding theoretical underpinnings of a specific method is pivotal for realizing the limits of its use. On the other hand, the theory separated from practical implementation tends to be a bit dry, so getting acquainted with data management and actual implementation of different models is also important.

Learning Objectives

• Knows the basic principles of scientific research
• Knows the basic principles of causal inference and Rubin Causal Model
• Knows the main stages of empirical project
• Knows how to set up and interpret linear regression
• Knows Gauss-Markov Theorem
• Knows how to address violations in Gauss-Markov Assumptions
• Can use logit and probit models for analysing data with binary dependent variables
• Can use event count models
• Can use discrete choice models
• Can use duration models
• Knows the basic principles of experimental and quasi-experimental research
• Knows statistical methods suitable for analyzing data that comes from natural experiments

Expected Learning Outcomes

• Be able to apply the Law of Large Numbers and the Central Limit Theorem.
• Able to to build up bivariate and multivariate linear regressions and estimate these models using ordinary least squares (OLS) estimator
• Students can explain why randomized experiment is the "golden standard" of causal analysis. They can design an experiment and prepare research program
• to know Gauss-Markov Theorem
• Code a Logit regression from scratch, run a classic Logit regression in Python, know alternative Logit regressions.
• Define and use the maximum likelihood estimation approach
• - Implement causal inference methods (matching, instrumental variables, regression discontinuity, difference-in-difference, fixed effects) - Identify which causal assumptions are necessary for each type of statistical method
• Learn how to apply difference-in-difference design in research
• Learn how to apply regression discontinuity design in research
• Know the basic principles of scientific research
• Know the basic elements of Rubin Causal Model
• Know the main steps of empirical research design
• Knows the theory behind difference-in-means testing and can use it to evaluate statistical hypotheses
• Can identify violations in Gauss-Markov assumptions and knows main approaches that address these violations
• Can code Poisson and Negative Binomial Regressions in Python
• Knows how to set up zero-inflated event-count models and the scope of their applicability
• Knows how to set up and interpret duration models

Course Contents

• Introduction
• Hypothesis Testing and Linear Regression
• Maximum Likelihood Estimation
• Causal Inference

Assessment Elements

• Midterm Exam
• Final exam
• Homeworks
• Attendance

Interim Assessment

• 2022/2023 4th module
  0.2 * Final exam + 0.2 * Midterm Exam + 0.05 * Attendance + 0.25 * Homeworks

Bibliography

Recommended Core Bibliography

• Designing social inquiry : scientific inference in qualitative research, King, G., 1994
• Econometric Analysis, 5th ed., 1026 p., Greene, W. H., 2003
• Introductory econometrics : a modern approach, Wooldridge, J. M., 2020
• Mostly harmless econometrics : an empiricist's companion, Angrist, J. D., 2009
• Principles of comparative politics, Clark, W. R., 2013
• The logic of scientific discovery, Popper, K. R., 1997

Recommended Additional Bibliography

• Counterfactuals and causal inference : methods and principles for social research, Morgan, S. L., 2012
• Statistical inference, Casella, G., 2002