• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Quantitative Data Analyses

2018/2019
Academic Year
ENG
Instruction in English
6
ECTS credits
Course type:
Elective course
When:
1 year, 3 module

Instructor

Course Syllabus

Abstract

The course gives 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. The topics covered by the course are diagnostics for linear models, binary, multilevel, ordered, and Poisson regression. 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

  • An introduction to a variety of extensions of linear regression analysis widely used in modern social sciences
  • Implementation of the statistical methods in R
Expected Learning Outcomes

Expected Learning Outcomes

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

Course Contents

  • Topic 1. Diagnostics for linear models
  • Topic 2. Binary logistic regression
  • Topic 3. Multinomial logistic regression
  • Topic 4. Ordered logistic regression
  • Topic 5. Poisson regression
Assessment Elements

Assessment Elements

  • non-blocking Test
  • non-blocking In-class Participation
  • non-blocking Exam (final project)
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.3 * Exam (final project) + 0.4 * In-class Participation + 0.3 * Test
Bibliography

Bibliography

Recommended Core Bibliography

  • Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=941245
  • An adventure in statistics: The reality enigma, Field, A., 2016

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. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.363067604
  • Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, & Maintainer Trevor Hastie. (2013). Type Package Title Data for An Introduction to Statistical Learning with Applications in R Version 1.0. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.28D80286
  • Hilbe, J. (2011). Negative Binomial Regression (Vol. 2nd ed). Cambridge: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=357443
  • 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