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# Quantitative Data Analyses

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
6
Кредиты
Статус:
Курс обязательный
Когда читается:
1-й курс, 4 модуль

### Course 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

• 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

• 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

• 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

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

#### Interim Assessment

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

#### 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