• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
Master 2018/2019

Methodology of Contemporary Asian Studies

Type: Compulsory course (Business and Politics in Modern Asia)
Area of studies: Asian and African Studies
When: 1 year, 1-4 module
Mode of studies: Full time
Instructors: Sergei Akopov, Veronika Kostenko, Olga Strebkova
Master’s programme: Business and Politics in Modern Asia
Language: English
ECTS credits: 6

Course Syllabus

Abstract

The course begins with the introduction to the basic principles of political inquiry. Then we consider the basic concepts of statistics and probability. We also discuss such topics as exploratory data analysis and data visualization, statistical hypothesis testing, linear and generalized linear regression models. 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

  • Provide a brief introduction to the methodology of political science research
Expected Learning Outcomes

Expected Learning Outcomes

  • Able to read (and understand!) most academic PS articles
  • Able to speak the language of data fluently
  • Able to understand by yourself and explain to others such words as ”variable”, ”distribution”, ”regression”, ”p-value”, etc.
  • Able to design a quantitative political study
  • Able to use R programming language for statistical computations
  • Able to choose statistical methods appropriate to your substantive research problem
Course Contents

Course Contents

  • Design Types, Data Types, and Data Summarization
  • Basic Statistical Concepts
  • Exploratory Data Analysis and Visualization
  • Inference and Hypothesis Testing
  • Simple Regression Methods
  • Confounding and Effect Modification (Interaction)
  • Multiple Regression Methods
Assessment Elements

Assessment Elements

  • non-blocking Homework (1-5)
    5 home assignments (cumulative grade – 10% for each task), late assignments will be graded down (one point on a 1-10 scale per day of delay)
  • non-blocking Essay
    Final project presentation (50%) Late assignments will be graded down (one point on a 1-10 scale per day of delay). If you plagiarize, you will fail. You may not recycle papers used in other classes.
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.5 * Essay + 0.5 * Homework (1-5)
  • Interim assessment (4 module)
    0.25 * Essay + 0.25 * Homework (1-5) + 0.5 * Interim assessment (2 module)
Bibliography

Bibliography

Recommended Core Bibliography

  • Wilcox R R. Understanding and Applying Basic Statistical Methods Using R / R R. Wilcox. - Hoboken, New Jersey: Wiley; 2016. eBook https://ebookcentral.proquest.com/lib/hselibraryebooks/detail.action?docID=4526801

Recommended Additional Bibliography

  • An adventure in statistics: The reality enigma, Field, A., 2016
  • Field, A. V. (DE-588)128714581, (DE-627)378310763, (DE-576)186310501, aut. (2012). Discovering statistics using R Andy Field, Jeremy Miles, Zoë Field. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.363067604