Master
2018/2019
Quantitative Data Analyses
Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type:
Elective course (Modern Social Analysis)
Area of studies:
Sociology
Delivered by:
Department of Sociology
When:
1 year, 3 module
Mode of studies:
offline
Instructors:
Viyaleta Korsunava
Master’s programme:
Modern Social Analysis
Language:
English
ECTS credits:
6
Contact hours:
28
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
- 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
- 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
- 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
Interim Assessment
- Interim assessment (3 module)0.3 * Exam (final project) + 0.4 * In-class Participation + 0.3 * Test
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