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Regular version of the site
Bachelor 2023/2024

Data Analysis in Sociology

Type: Compulsory course (Sociology and Social Informatics)
Area of studies: Sociology
When: 3 year, 3, 4 module
Mode of studies: distance learning
Open to: students of one campus
Instructors: Ksenia Tenisheva
Language: English
ECTS credits: 4
Contact hours: 46

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

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

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

Course Contents

  • Central tendency measures
  • Chi-square
  • Two means comparison
  • One-way ANOVA
  • Linear regression
  • Preliminary data analysis
  • Linear regression
  • Introduction to GLM
  • Exploratory factor analysis
Assessment Elements

Assessment Elements

  • non-blocking Individual home assignments
  • non-blocking Mid-term test
  • blocking Final exam (blocking)
  • non-blocking Projects
  • non-blocking In-class activity
  • non-blocking Mid-Term Test
  • non-blocking Short tests
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • 2022/2023 4th module
    0.1 * Exam + 0.1 * Exam + 0.1 * In-class activity + 0.1 * In-class activity + 0.1 * In-class activity + 0.1 * In-class activity + 0.2 * Mid-Term Test + 0.2 * Mid-Term Test + 0.4 * Projects + 0.4 * Projects + 0.1 * Short tests + 0.1 * Short tests
  • 2023/2024 4th module
    0.3 * Final exam (blocking) + 0.3 * Final exam (blocking) + 0.225 * Individual home assignments + 0.225 * Individual home assignments + 0.225 * Individual home assignments + 0.225 * Individual home assignments + 0.125 * Mid-term test + 0.125 * Mid-term test + 0.125 * Mid-term test + 0.125 * Mid-term test
  • 2024/2025 3rd module
    0.1 * In-class activity + 0.1 * In-class activity + 0.6 * Individual home assignments + 0.6 * Individual home assignments + 0.3 * Projects + 0.3 * Projects
Bibliography

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

  • TENISHEVA Kseniia ALEKSEEVNA
  • Ильина Мария Ивановна