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Course Syllabus

Master 2021/2022

Quantitative Data Analyses

Category 'Best Course for Career Development'
Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (Modern Social Analysis)
Area of studies: Sociology
Delivered by: Department of Sociology
Where: Saint-Petersburg School of Social Sciences
When: 1 year, 4 module
Mode of studies: offline
Open to: students of one campus
Instructors: Viyaleta Korsunava
Master’s programme: Modern Social Analysis
Language: English
ECTS credits: 6
Contact hours: 34

Course Syllabus

Full Syllabus

Abstract

The course covers different types of regression modeling, including further insights into linear regression and diagnostics for linear models, binary, multinomial, ordered regression, models for count data, along with causal inference methods. Students are assumed to have basic knowledge of statistics and be familiar with several conventional statistical methods, most importantly linear regression, and with the R programming environment.
Learning Objectives

Learning Objectives

  • The main objective of the course is to give an introduction to a variety of extensions of linear regression analysis widely used in modern social sciences, as well as implementations of the respective methods in R, a popular, free programming language for statistical computing. By the end of the course, students will be able to choose relevant methods of analysis, implement necessary techniques, and interpret the results of modeling.
Expected Learning Outcomes

Expected Learning Outcomes

  • Able to choose statistical methods appropriate to their data and substantive research problem
  • Able to design a quantitative social study
  • Able to read and understand most academic social sciences articles that use quantitative approach
  • Able to use R programming language for complex statistical computations
Course Contents

Course Contents

  • Advanced analysis with linear regression
  • Binary logistic regression
  • Multinomial logistic regression
  • Ordered logistic regression
  • Models for count data
  • Introduction to causal inference: Instrumental variables
  • Regression discontinuity design
Assessment Elements

Assessment Elements

  • non-blocking Test 1
  • non-blocking Test 2
  • non-blocking In-class assignments
    An unweighted average of grades for in-class assignments.
  • non-blocking Final exam
    The exam is held online (in Skype) in the form of a test covering all topics.
Interim Assessment

Interim Assessment

  • 2021/2022 4th module
    0.2 * Test 2 + 0.2 * Final exam + 0.2 * Test 1 + 0.4 * In-class assignments
Bibliography

Bibliography

Recommended Core Bibliography

  • Agresti, A., & Finlay, B. (2014). Statistical Methods for the Social Sciences: Pearson New International Edition (Vol. Pearson new international ed., 4. ed). Harlow England: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418314
  • 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
  • Morgan, S. L., & Winship, C. (2007). Counterfactuals and Causal Inference : Methods and Principles for Social Research. New York: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=206937

Recommended Additional Bibliography

  • Chatterjee, S., Hadi, A. S., & Ebooks Corporation. (2012). Regression Analysis by Example (Vol. Fifth edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=959808
  • Freund, R. J., Wilson, W. J., & Sa, P. (2006). Regression Analysis (Vol. 2nd ed). AMsterdam: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=320724
  • Guido Imbens, & Thomas Lemieux. (2007). Regression Discontinuity Designs: A Guide to Practice.
  • I. Rohlfing. (2012). Case Studies and Causal Inference : An Integrative Framework. Palgrave Macmillan.
  • Jiang, J. (2007). Linear and Generalized Linear Mixed Models and Their Applications. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=212826
  • Upton, G. J. G. (2016). Categorical Data Analysis by Example. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1402878

Authors

  • KORSUNOVA VIOLETTA IGOREVNA