Data Analysis in Sociology

2026/2027

Учебный год

ENG

Обучение ведется на английском языке

5

Кредиты

Кто читает:

Департамент социологии

Статус:

Курс обязательный

Когда читается:

2-й курс, 3, 4 модуль

Course Syllabus

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

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

Assessment Elements

• Group projects
• Final exam
• Mid-term test
• In-class participation (seminars)
• Practical homeworks

Interim Assessment

• 2026/2027 4th module
  0.15 * In-class participation (seminars) + 0.15 * Practical homeworks + 0.3 * Final exam + 0.2 * Mid-term test + 0.2 * Group projects
• 2027/2028 3rd module
  1 * 2026/2027 4th module
• 2028/2029 3rd module
  1 * 2026/2027 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

• Lebedev Daniil Vadimovich
• TSIGEMAN-GORENKO Elina Sergeevna