Methodology and Research Methods of Political Science
Instruction in English
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.
- Know the basics of probability theory
- Know fundamental concepts and ideas from mathematical statistics
- Know major components of political science research
- Being able to build research designs and write research papers that address questions from political science and economics
- Know the theory behind OLS regression and its main assumptions
- Being able to perform basic data management tasks such as merging and reshaping datasets
- Being able to implement OLS regression with the tools of Python or R language
- Being able to perform basic visualizations that illustrate results of regression analysis and summary statistics
- Know main maximum likelihood models that are frequently used in political science and economics
- Know the principles of scientific research
- Know the main steps of scientific inference
- Know basic logical components of theory building
- Know the main steps of research design in political science
- Know how to find research designs that best fit research questions and theory
- Know the stability assumption (SUTVA)
- Able to analyze experimental designs for their correspondence to SUTVA
- Know criteria for classification of assignment mechanisms
- Know the definition of assignment mechanism
- Able to classify assignment mechanisms in accordance with individualistic, probabilistic and unconfounded properties
- Know the definition of probability space and substantive interpretation of the term
- Know Kolmogorov axioms and definition of event
- Know the concepts of conditional probability and joint probability
- Know the law of total probability
- Know the basics of data managements in python, such as reshaping and merging datasets
- Know the definition of a random variable
- Know the difference between discrete and continuous random variables
- Able to derive moments of random variables from moment generating functions and characteristic functions
- Know the concepts of covariance and correlation
- Know how to derive distributions of sums of random variables
- Know basic operations with random vectors and random matrices
- Know the properties of a random sample
- Know the properties of a sampling distribution
- Able to construct a random sample in accordance with basic algorithms
- Know basic concepts of random variable convergence: almost sure, in probability, and in distribution
- Know the statement of the theorem for the Strong and Weak Laws of Large Numbers
- Know the statement of the Central Limit Theorem
- Able to illustrate Laws of Large Numbers and Central Limit Theorem using tools from Python or R
- Know the basic ideas behind statistical estimation
- Know the fundamental properties used to evaluate statistical estimators: consistency, unbiasedness, efficiency
- Know the ideas behind basic approaches to derivation of estimators: Method of Moments, MLE, Least Squares, and Bayes
- Know the ideas behind difference-in-means test
- Know how to derive confidence intervals and compute p-values
- Know how to implement difference-in-means test in Python or R
- Know how to substantively interpret and illustrate results from bivariate regression
- Know how to derive OLS estimates for bivariate regression
- Able to perform bivariate regression analysis with the tools of Python or R language
- Know the basic ideas behind multiple regression analysis
- Know how to derive OLS estimates for multiple regression using matrix notation
- Know Gauss-Markov assumptions
- Able to perform multiple regression analysis and appropriate visualizations in Python or R
- Know the full proof of Gauss-Markov Theorem
- Know the asymptotic properties of OLS estimators
- Know how to perform hypothesis testing and how to derive confidence intervals in multiple regression settings
- Know how to deal with violations in standard Gauss-Markov assumptions
- Know the consequences of violations in standard Gauss-Markov assumptions
- Able to perform basic tests for violations in Gauss-Markov assumption in Python or R
- Know the basic families of MLE models
- Know how to derive logit and probit estimates using MLE framework
- Able to implement event count and discrete choice models in Python or R
- Able to substantively interpret results from basic MLE models
- IntroductionIntroduction. Course overview. Principles of scientific research.
- Research DesignResearch design in political science. Research questions, principles of theorizing and data collection.
- Elements of Causal Inference & Rubin Causal ModelCausal inference and Rubin Causal Model. Statement of the problem that causal inference seeks to solve. Assumptions of causal inference. Potential outcomes. Assignment mechanisms. Stability assumption (SUTVA). Introduction to randomization.
- Basics of Probability TheoryProbability spaces. Events. Kolmogorov axioms. Conditional probability. The law of total probability. Independence.
- Random Variables and Their TypesDefinition of a random variable. Discrete and continuous random variables. Distributions of random variables. Transformations and expectations of random variables. Moments and central moments of random variables. Moment generating functions and characteristic functions.
- Multiple Random VariablesMultiple random variables. Joint and marginal distributions. Conditional distributions. Covariance and correlation. The law of iterated expectation. Laws of total variance and total covariance.
- Populations and SamplesRandom sampling from a population. Properties of a random sample. Sampling distribution and its properties.
- Central Limit Theorem and the Law of Large NumbersConvergence of random variables. Almost sure convergence. Convergence in probability. Convergence in distribution. Strong and weak laws of large numbers. Central Limit Theorem.
- Statistical Estimators and Their PropertiesDefinition of a statistical estimator. Properties of estimators. Unbiased estimators. Consistent estimators. Efficient estimators. General approaches to derivation of estimators.
- Difference-in-Means and Hypotheses TestingDifference-in-means test. Confidence interval. Hypothesis testing and p-values. Type I and type II errors.
- Bivariate Linear Regression and Its InterpretationOLS estimation of bivariate regression. Interpretation of bivariate regression results. Implementation of bivariate regression in Python.
- Multiple Linear Regression and Gauss-Markov Theorem IIntroduction to multiple regression. Multiple regression in matrix form. OLS estimation of multiple regression. Gauss-Markov Theorem. Implementation of bivariate regression in Python.
- Multiple Linear Regression and Gauss-Markov Theorem IIInference in multiple regressions. Hypotheses testing and confidence intervals. Asymptotic properties of OLS estimators.
- Multiple Linear Regression – Violations in AssumptionsViolations of standard Gauss-Markov assumptions. Heteroskedasticity. Model misspecification. Non-zero expectation of error terms.
- Introduction to MLE modelsMaximum Likelihood Estimation (MLE) in economics and political science. Common families of MLE models. Discrete choice and event count models. MLE estimation of binary choice models.
- Wrap-up and Final ReviewWrap-up of the class. Final Exam Review.
- Домашнее Задание 1 (неблокирующий)
- Домашнее Задание 2 (неблокирующий)
- Домашнее Задание 3 (неблокирующий)
- Домашнее Задание 4 (неблокирующий)
- Домашнее Задание 5 (неблокирующий)
- Домашнее Задание 6 (неблокирующий)
- Домашнее Задание 7 (неблокирующий)
- Домашнее Задание 8 (неблокирующий)
- Промежуточный Экзамен (неблокирующий)
- Итоговый Экзамен (неблокирующий)
- Активность на Занятиях (неблокирующий)
- Промежуточная аттестация (3 модуль)0.1 * Активность на Занятиях + 0.06 * Домашнее Задание 1 + 0.06 * Домашнее Задание 2 + 0.06 * Домашнее Задание 3 + 0.06 * Домашнее Задание 4 + 0.06 * Домашнее Задание 5 + 0.06 * Домашнее Задание 6 + 0.06 * Домашнее Задание 7 + 0.06 * Домашнее Задание 8 + 0.22 * Итоговый Экзамен + 0.2 * Промежуточный Экзамен
- Designing social inquiry : scientific inference in qualitative research, King G., Keohane R. O., 1994
- Econometric analysis of cross section and panel data, Wooldridge J. M., 2002
- Introductory econometrics : a modern approach, Wooldridge J. M., 2013
- Principles of comparative politics, Clark W. R., Golder M., 2013
- Statistical inference, Casella G., Berger R. L., 2002
- Statistical inference, Casella G., Berger R. L., 2002
- The logic of scientific discovery, Popper K. R., 1997
- Econometric analysis, Greene W. H., 2000