Delivered at:: Department of Sociology

Course type:: Compulsory course

When:: 2 year, 3, 4 module

Instructor

Lebedev, Daniil

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Abstract

This course lasts for three years. The 1st year aims at beginners. This year starts from introductory topics (variable types, hypothesis testing, descriptive statistics) to working with some methods (chi-square, t-test, nonparametric statistics, one-way ANOVA, and linear regression). The course covers the building blocks of quantitative data analysis with the aim to train students to be informed producers and consumers of quantitative research. The applied part introduces working in R (RStudio) for calculations and reporting.This course is the starting point for social science and humanities students interested in pursuing training in advanced methods of data analysis or planning to use quantitative methods in their own research. The 2nd year aims at intermediate-level students. This year starts from introductory topics (data preparation, visualization, basic statistical tests) to working with more advanced methods of data analysis (interaction effects in linear regression, GLM, factor analysis). The course aims to develop quantitative data analysis skills required to understand and perform independent research. The applied part includes working in R (RStudio). The 3d year aims at upper-intermediate level students.

Learning Objectives

Develop skills necessary to prepare and present social data. Develop skills necessary to perform data analysis using social data in the R software environment.

Expected Learning Outcomes

Have skills to write R code for basic data analysis tasks
Have skills in using R Studio for statistical data analysis
Be able to apply data analysis tools to real-life problems.
Able to use R programming language for statistical computations
Be able to solve the problems of data analysis competitions

Course Contents

Preliminary data analysis
Central tendency measures
Chi-square
Two means comparison
One-way ANOVA
Linear regression
Linear regression
Linear regression with multiple predictors
Linear regression: OLS. Diagnostics
Introduction to GLM
Exploratory factor analysis
Confirmatory factor analysis
Data quality. Main issues with data
Resampling
Missings treatment
Decision trees
Cluster analysis
Advanced regression techniques
Multilevel regression

Assessment Elements

In-class participation and practice
Final exam
The exam is aimed at testing the student's theoretical knowledge of the main concepts and statistical rules, interpretation of the statistical output, as well as skills in working in R statistical software to solve several practical tasks. The exam will be held with the HSE proctoring system.
Group projects
Mid-term test

Interim Assessment

2025/2026 4th module
0.4 * Final exam + 0.2 * Group projects + 0.2 * In-class participation and practice + 0.2 * Mid-term test
2026/2027 4th module
1 * 2025/2026 4th module
2027/2028 3rd module
1 * 2025/2026 4th module

Bibliography

Recommended Core 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
Discovering statistics using R, Field, A., 2012
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

Recommended Additional Bibliography

Applied regression analysis and generalized linear models, Fox, J., 2008
Regression analysis of count data, Cameron, A. C., 2013

Authors

TSIGEMAN-GORENKO Elina Sergeevna
TKACHEVA TATYANA YUREVNA

Bachelor’s Programme 'Sociology and Social Informatics'

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Data Analysis in Sociology