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# Data Analysis in Sociology

2023/2024
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты
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 solve typical data analysis problems on social data in the R software environment

#### Expected Learning Outcomes

• Choose appropriate methods and techniques for certain types of variables and certain aims of the analysis
• Conduct statistical analyses in RStudio
• Create analytical reports describing all the stages of analysis and interpreting its results
• Give meaningful interpretation of statistical results: regression coefficients, tables, plots and diagrams (produced in R)
• Perform data transformations
• Represent graphically the results of the statistical analyses

#### Course Contents

• Central tendency measures
• Chi-square
• Two means comparison
• One-way ANOVA
• Linear regression
• Linear regression with multiple predictors
• Data quality. Main issues with data
• Missings treatment
• Decision trees
• Cluster analysis
• Multilevel regression
• 3rd year topics
• 4th year topics

#### Assessment Elements

• In-class participation and practice
• Mid-term test
• Final exam (blocking)
• Group projects

#### Interim Assessment

• 2023/2024 4th module
0.3 * Final exam (blocking) + 0.3 * Final exam (blocking) + 0.3 * Group projects + 0.3 * Group projects + 0.2 * In-class participation and practice + 0.2 * In-class participation and practice + 0.2 * Mid-term test + 0.2 * Mid-term test
• 2024/2025 4th module
1 * 2023/2024 4th module + 1 * 2023/2024 4th module
• 2025/2026 3rd module
1 * 2023/2024 4th module + 1 * 2023/2024 4th module

#### Recommended Core Bibliography

• Denis, D. J. (2016). Applied Univariate, Bivariate, and Multivariate Statistics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1091881
• Tabachnick, B. G., & Fidell, L. S. (2014). Using Multivariate Statistics: Pearson New International Edition (Vol. 6th ed). Harlow, Essex: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418064