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Regular version of the site

Research Seminar "Quantitative Methods in Political Research"

2020/2021
Academic Year
ENG
Instruction in English
5
ECTS credits
Course type:
Elective course
When:
1 year, 3, 4 module

Instructor

Course Syllabus

Abstract

This research seminar offers an overview of the key quantitative methods used in contemporary political science and helps students to master their use for their own research. It considers the basic concepts of statistics and probability. We also discuss such topics as exploratory data analysis and data visualization, statistical hypothesis testing, linear regression models, and regression diagnostics, generalized linear models, and the potential outcomes framework for causal inference. R programming language is used as a primary tool for data processing and statistical computations. Students are assumed to be familiar with high school math program, have basic computer literacy and be willing to work hard to learn the essentials of data analysis.
Learning Objectives

Learning Objectives

  • To acquaint students with statistical methods and terminology
  • To teach students how to implement statistical methods using R programming language.
Expected Learning Outcomes

Expected Learning Outcomes

  • reads (and understands) most academic PS articles
  • speaks the language of data fluently
  • designs a quantitative political study
  • chooses statistical methods appropriate to his substantive research problem
  • uses R programming language for statistical computations
Course Contents

Course Contents

  • Design types, data types, and data summarization
    1.1. Prospective cohort studies, case/control studies, cross-sectional studies 1.2. Quantitative research design 1.3. Continuous and nominal-categorical data, ordered data, summary statistics, visualization 1.4. Try R, R as calculator, basic R functions, creation of the data frame
  • Basic Statistical Concepts
    2.1. Population vs. Sample 2.2. Random Sample 2.3. Probability, probability distributions 2.4. Work with R functions: pnorm, tnorm, density, etc.
  • Exploratory Data Analysis and Visualization
    3.1. Work with R: set working directory, read your data, data types in R 3.2. Data transformation, missing values, outliers 3.3. Visualization: histograms, barplots, boxplots
  • Inference and Hypothesis Testing
    4.1. Normal distribution, central limit theorem 4.2. Null hypothesis, alternative hypothesis 4.3. Type I error, type II error, P-value 4.4. Confidence intervals 4.5. Causal inference
  • Simple Regression Methods
    5.1. Linear regression assumptions 5.2. OLS regression, regression coefficients, significance level alpha 5.3. OLS regression example in R: syntax and interpretation, visualization of the result
  • Confounding and Effect Modification (Interaction)
    6.1. Define and identify confounding 6.2. Crude and adjusted measures 6.3. An example of effect modification 6.4. Define and identify interaction effect 6.5. An example of interaction effect
  • Multiple Regression Methods
    7.1. Diagnostic plots 7.2. Comparing model 7.3. Variable selection 7.4. Tools for summarizing and visualizing regression models
  • Generalized Linear Models 1
    8.1. Logit regression 8.2. Poisson regression 8.3. Fitting and visualizing Logit and Poisson regressions using R
  • Generalized Linear Models 2
    9.1. Ordered regressions 9.2. Multinomial regression 9.3. Fitting and visualizing Ordered and Multinomial regressions using R
Assessment Elements

Assessment Elements

  • non-blocking Homework (1-5)
    The most important aspects of assignments that affect grades are following: a) correctness of answers to questions given in an assignment, b) ability to write R code correctly (if necessary), c) appropriate use of statistical language, d) correctness of results’ interpretations. If all these criterions are met, you can expect an excellent grade (8-10 on 0-10 scale). Late assignments will be graded down by 1 point for each day of delay (but no more than 3 points in total). Plagiarism is prohibited.
  • non-blocking In-class Participation
  • non-blocking Midterm paper
    The most important aspects of the paper to be graded are: 1) logical reasoning, 2) correctness and efficiency of R code written, 3) accuracy of statistical methods and models used, 4) correctness and creativity of results’ interpretation, 5) fluency and accuracy of statistical terminology used.
  • non-blocking Final paper
    The most important aspects of the paper to be graded are: 1) logical reasoning, 2) correctness and efficiency of R code written, 3) accuracy of statistical methods and models used, 4) correctness and creativity of results’ interpretation, 5) fluency and accuracy of statistical terminology used.
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.3 * Final paper + 0.25 * Homework (1-5) + 0.25 * In-class Participation + 0.2 * Midterm paper
Bibliography

Bibliography

Recommended Core Bibliography

  • Wilcox, R. R. (2016). Understanding and Applying Basic Statistical Methods Using R. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1237377

Recommended Additional Bibliography

  • Field, A. V. (DE-588)128714581, (DE-627)378310763, (DE-576)186310501, aut. (2012). Discovering statistics using R Andy Field, Jeremy Miles, Zoë Field.
  • Val Todd. (2017). Field and Iles (2016) An Adventure in Statistics: The Reality Enigma. PRISM, 1(1), 195–199. https://doi.org/10.24377/LJMU.prism.vol1iss1article304