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Regular version of the site
Master 2018/2019

Quantitative Methods in Social Research

Type: Compulsory course (Political Analysis and Public Policy)
Area of studies: Political Science
When: 1 year, 1, 2 module
Mode of studies: offline
Instructors: Elena Artyukhova, Sergey Parkhomenko
Master’s programme: Political Analysis and Public Policy
Language: English
ECTS credits: 6

Course Syllabus

Abstract

Since understanding statistics is essential to understand research in the social and behavioral sciences, the course introduces to the students the notions of the basic statistical tools, how to calculate the basics of statistics and how to evaluate them. This is elementary course and students without strong mathematical knowledge will have no difficulties to follow it.
Learning Objectives

Learning Objectives

  • to teach students how to analyze data using quantitative methods
Expected Learning Outcomes

Expected Learning Outcomes

  • As a results, students should know types and particular properties of quantitative data
  • As a results, students should know basic methods of descriptive and inferential statistics, probability theory, linear regression and correlation analysis
  • As a results, students should be able to describe any data sets using a few numbers (descriptive statistics), and then reach statistically justifiable conclusions about those data sets
  • As a results, students should be able to define and reconstruct the system of connections between different factors in the sphere of public policy and human rights
  • As a results, students should be able to selects appropriate model / method of statistical analysis for a given problem and they should know linear regression and correlation analysis
  • As a results, students should have a confidence in their ability to tackle basic applied statistics problems
  • As a results, students should know the fundamental knowledge needed to learn more in-depth statistical theory
Course Contents

Course Contents

  • Data Collection
    The first session consists of the Lecture and the Seminar. The Lecture will introduce the main concepts of quantitative methods in social research and lay out the course plan and requirements from student participation. During the Seminar we will find out different quantitative data collection methods and available data sources.
  • Describing Data
    This session consists of the Lecture and the Seminar. During the Lecture we will learn basic statistics for numerical variables. During the Seminar we will practice in calculating basic descriptive statistics in Excel.
  • Probability concepts
    This session consists of the Lecture and the Seminar. The Lecture will introduce basic concepts of the Probability theory. During the Seminar we will practice in probabilities calculating in Excel and R.
  • Estimating Population Parameters
    This session consists of the Lecture and the Seminar. During the Lecture we will learn types of sampling distributions and notions of estimating population parameters and hypothesis testing. During the Seminar we will practice in estimating population parameters and hypothesis testing in R and in analyzing variance in Excel.
  • Linear Regression and Correlation
    This session consists of the Lecture and the Seminar. During the lecture we will learn linear regression and correlation analysis. During the Seminar we will practice in correlation and regression analysis in Excel and R.
  • Networks
    This session consists of the Lecture and the Seminar. During the Lecture we will learn basic concepts of networks, networks visualization, and methods of network analysis. During the Seminar we will practice in network analysis and visualization in R.
  • Clustering
    This session consists of the Lecture and the Seminar. During the Lecture we will learn basic methods of clasterization. During the Seminar we will practice in clustering procedures in R.
Assessment Elements

Assessment Elements

  • non-blocking Homeworks
  • non-blocking Quizzes
  • non-blocking Course Project
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.4 * Course Project + 0.4 * Homeworks + 0.2 * Quizzes
Bibliography

Bibliography

Recommended Core Bibliography

  • Newbold, P., Carlson, W. L., & Thorne, B. (2013). Statistics for Business and Economics: Global Edition (Vol. Eight edition). Boston, Massachusetts: Pearson Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1417883

Recommended Additional Bibliography

  • Jan Ubøe. (2017). Introductory Statistics for Business and Economics. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sptbec.978.3.319.70936.9