Quantitative Methods in Political Science (2235.YR.013002.1)

General information

Type:	OPT	Curs:	2	Period:	S semester
ECTS Credits:	6 ECTS

Teaching Staff:

Group	Teacher	Department	Language
Year 2	Vladimir Manaev	Economía, Finanzas y Contabilidad	ENG

Students should be able to follow basic algebra.

No previous knowledge of statistics is assumed.

The course has 3 main goals:
1. Help students to understand, analyze and interpret political data.
2. To provide students with all the necessary skills to effectively perform quantitative research in the political science area.
3. To develop the analytical skills that will help students to critically analyze political science phenomena and derive evidence-based conclusions.

By the end of the course, students are expected to be able to:
- Classify different types of variables (categorical, ordinal, numerical) and datasets
(cross-section, time series, panel).
- Understand and produce commonly used graphical representations of data, such
as histograms, pie charts, etc...
- Interpret and calculate summary statistics for a given dataset.
- Understand the concept of random experiments and the main laws of
probability.
- Recognize the difference between samples and populations and the need to use
sample statistics to make inferences.
- Interpret the output of frequently used statistical instruments such as
confidence intervals, hypothesis tests, p-values, etc?
- Understand the concept of correlation, causality, and statistical independence.
- Identify different types of relationships between different variables in a dataset.
- Recognize the advantages and drawbacks of using certain statistical techniques
in different practical situations.

1. Unit 1. Intro to Statistics. The role of Statistics in the Social Sciences. Descriptive and inferential statistics. Types of variables: categorical vs. numeric and qualitative vs. quantitative. Basic graphs. Understanding boxplots and histograms.

2. Unit 2. Descriptive Statistics. Measures of central tendency: mean, median, mode. Percentiles. Quartiles. Measures of dispersion: range, interquartile range, variance, standard deviation. Two variables: scatter plots, covariance, and correlation.

3. Unit 3. Probability and Random Variables. Random experiments. Sample space. Fundamental laws of Probability. Conditional probability and Bayes Rule. Introduction to Random Variables. Discrete random variables. Probability mass function. Distribution function. Expectation and Variance. Discrete distributions: Uniform, Bernoulli, and Binomial distributions Continuous Random Variables. Density and distribution functions. Quantiles. Expectation and Variance. The uniform and the exponential distributions. Normal distribution. Standardization.

4. Unit 4. Estimation and Inference. Sampling revisited. Sampling distribution of the sample mean. Central Limit Theorem and Law of Large Numbers. Point estimation. Bias and efficiency of a point estimator. Confidence intervals with known and unknown population variance. The t-Student distribution. Hypothesis testing. Hypothesis tests for the mean. One and two-tailed tests. Confidence level and p-value. Hypothesis tests for the mean with paired data. Test for difference in means. Type I and Type II errors.

5. Unit 5. Linear Regression. Simple Linear Regression. Estimation of the coefficients. R-squared and ANOVA. Distribution of the estimated coefficients. Hypothesis testing. Omitted Variable Bias. Causality Multiple regression. F-test and interpretation of the coefficients.

The course will combine both theoretical and practical studies.

ASSESSMENT BREAKDOWN

Main textbook: Alan Agresti, "Statistical Methods for the Social Sciences", 5th Global Edition, Pearson, 2018.

Additional:
- Marchant-Shapiro, T. Statistics for Political Analysis: Understanding the
Numbers. CQ Press, 2014.
- Moore, David S., George P. McCabe, and Bruce A. Craig. Introduction to the
practice of Statistics. New York: W.H. Freeman, 2009.

Group	Teacher	Department
Year 2	Vladimir Manaev	Economía, Finanzas y Contabilidad